Author: "Olivier Aycard" / Topic: computer vision - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Olivier Aycard"' showing total 33 results

Start Over Author "Olivier Aycard" Topic computer vision

33 results on '"Olivier Aycard"'

1. Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks

Author: Olivier Aycard, Philip Scales, Mykhailo Rimel, Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP), Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Artificial Intelligence and Robotics (Marvin ), École nationale supérieure d'informatique et de mathématiques appliquées (ENSIMAG), Université Joseph Fourier - Grenoble 1 (UJF), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: Visual-based Localization, business.industry, Computer science, Global localization, Ceiling (cloud), Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Mobile Robot, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, business, CNN
Abstract: International audience; The problem of global localization consists in determining the position of a mobile robot inside its environment without any prior knowledge of its position. Existing approaches for indoor localization present drawbacks such as the need to prepare the environment, dependency on specific features of the environment, and high quality sensor and computing hardware requirements. We focus on ceiling-based localization that is usable in crowded areas and does not require expensive hardware. While the global goal of our research is to develop a complete robust global indoor localization framework for a wheeled mobile robot, in this paper we focus on one part of this framework-being able to determine a robot's pose (2-DoF position plus orientation) from a single ceiling image. We use convolutional neural networks to learn the correspondence between a single image of the ceiling of the room, and the mobile robot's pose. We conduct experiments in real-world indoor environments that are significantly larger than those used in state of the art learning-based 6-DoF pose estimation methods. In spite of the difference in environment size, our method yields comparable accuracy.
Published: 2021
Full Text: View/download PDF

2. A new tool to initialize global localization for a mobile robot

Author: Christophe Brouard, Olivier Aycard, Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: Computer science, business.industry, 020208 electrical & electronic engineering, pattern recognition, Process (computing), Mobile robot, 02 engineering and technology, perception, localization, Set (abstract data type), Position (vector), Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Index Terms-mobile robots, Artificial intelligence, information retrieval, business
Abstract: International audience; Localization is the ability for a mobile robot to know its position at all times. When the initial position is unknown, the localization process has to manage several possible positions that could correspond to the real one. The main drawback of this technique is the cost of computational complexity that could be high. In this paper, we present a new way to determine the set of initial possible positions that is fast (less than 3s) and enables to start the localization process with a small number of possible positions. The consequence is that our localization process determines the real position in a fast and robust way. Experimental results show the benefits of the method.
Published: 2020

3. An Evidential Filter for Indoor Navigation of a Mobile Robot in Dynamic Environment

Author: Michèle Rombaut, Denis Pellerin, Olivier Aycard, Catherine Garbay, Quentin Labourey, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA ), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)
Subjects: 0209 industrial biotechnology, Personal robot, Modality (human–computer interaction), Social robot, Computer science, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Mobile robot, 02 engineering and technology, Grid, Mobile robot navigation, 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Filter (video), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Robot, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Computer vision, Artificial intelligence, business, Active multimodal perception, Evidential filtering
Abstract: International audience; Robots are destined to live with humans and perform tasks for them. In order to do that, an adapted representation of the world including human detection is required. Evidential grids enable the robot to handle partial information and ignorance, which can be useful in various situations. This paper deals with an audiovisual perception scheme of a robot in indoor environment (apartment, house..). As the robot moves, it must take into account its environment and the humans in presence. This article presents the key-stages of the multimodal fusion: an evidential grid is built from each modality using a modified Dempster combination, and a temporal fusion is made using an evidential filter based on an adapted version of the generalized bayesian theorem. This enables the robot to keep track of the state of its environment. A decision can then be made on the next move of the robot depending on the robot's mission and the extracted information. The system is tested on a simulated environment under realistic conditions.
Published: 2016
Full Text: View/download PDF

4. Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

Author: Olivier Aycard, Ricardo Omar Chavez-Garcia, Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), European Project: 246587,EC:FP7:ICT,FP7-ICT-2009-4,INTERACTIVE(2010), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Engineering, Vehicle Safety, Advanced driver assistance systems, 02 engineering and technology, Simultaneous localization and mapping, Classifi-cation algorithms, law.invention, law, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Radar, Vehicle detection, 050210 logistics & transportation, Sensor fusion, Radar tracker, business.industry, Mechanical Engineering, 05 social sciences, Representation (systemics), Object (computer science), Object detection, Computer Science Applications, Index Terms—Intelligent vehicles, Automotive Engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract: International audience; —The accurate detection and classification of moving objects is a critical aspect of Advanced Driver Assistance Systems (ADAS). We believe that by including the objects classification from multiple sensors detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second , we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project which includes three main sensors: radar, lidar and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car and truck.
Published: 2015
Full Text: View/download PDF

5. Fusion at Detection Level for Frontal Object Perception

Author: Trung Dung Vu, Olivier Aycard, R. Omar Chavez-Garcia, Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), This work was also supported by the European Commission under interactIVe (http://www.interactive-ip.eu), a large scale integrating project part of the FP7-ICT for Safety and Energy Efficiency in Mobility., European Project, Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes
Subjects: 0209 industrial biotechnology, Computer science, media_common.quotation_subject, lidar processing, Image processing, 02 engineering and technology, Tracking (particle physics), Task (project management), law.invention, evidential theory, Reduction (complexity), 020901 industrial engineering & automation, Position (vector), law, Perception, 0502 economics and business, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, multi-sensor, Radar, media_common, 050210 logistics & transportation, business.industry, 05 social sciences, Vehicle perception, image processing, Lidar, Artificial intelligence, Information fusion, business
Abstract: International audience; Intelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects.
Published: 2014

6. Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

Author: Trung-Dung Vu, Fabio Tango, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Centro Ricerche Fiat (CRF), Centro Ricerche Fiat, European Project, and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Data processing, Engineering, Radar tracker, business.industry, Sensor fusion, Object detection, law.invention, Vehicle dynamics, law, Video tracking, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, Radar, business, Intelligent transportation system
Abstract: International audience; The paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented.
Published: 2014

7. Layer-based supervised classification of moving objects in outdoor dynamic environment using 3D laser scanner

Author: Asma Azim and Olivier Aycard
Subjects: Boosting (machine learning), Laser scanning, Computer science, business.industry, Feature extraction, Supervised learning, Pattern recognition, Linear classifier, Computer vision, Artificial intelligence, 3d laser scanner, business
Abstract: In this paper, we present a layered approach for classification of moving objects from 3D range data based on supervised learning technique. Our approach combines the model based classification in 2D with boosting for classifying the objects into four classes of interest namely bus, car, bike and pedestrian. In contrast to most of the existing work on 3D classification which involves extensive feature extraction and description, this combination uses simple single-valued features and allows our system to perform efficiently. The proposed method can be used in conjunction with any type of range sensors, however, we have demonstrated its performance using the data acquired from a Velodyne HDL-64E laser scanner.
Published: 2014
Full Text: View/download PDF

8. Detection, classification and tracking of moving objects in a 3D environment

Author: Olivier Aycard, Asma Azim, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: 0209 industrial biotechnology, Occupancy grid mapping, Computer science, Vehicle Environment Perception, 02 engineering and technology, Simultaneous localization and mapping, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Octree, Lidar Signal Processing, 020901 industrial engineering & automation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Automated Vehicles, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Representation (mathematics), Radar, Contextual image classification, business.industry, Object detection, Video tracking, Image, A priori and a posteriori, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract: Poster; International audience; In this paper, we present a framework based on 3D range data to solve the problem of simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic environments. The basic idea is to use an octree based Occupancy Grid representation to model dynamic environment surrounding the vehicle and to detect moving objects based on inconsistencies between scans. The proposed method for the discrimination between moving and stationary objects without a priori knowledge of the targets is the main contribution of this paper. Moreover, the detected moving objects are classified and tracked using Global Nearest Neighbor (GNN) technique. The proposed method can be used in conjunction with any type of range sensors however we have demonstrated it using the data acquired from a Velodyne HDL-64E LIDAR sensor. The merit of our approach is that it allows for an efficient three dimensional representation of a dynamic environment, keeping in view the enormous amount of information provided by 3D range sensors.
Published: 2012

9. Improving moving objects tracking using road model for laser data

Author: Qadeer Baig, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Engineering, Occupancy grid mapping, Laser scanning, 02 engineering and technology, Simultaneous localization and mapping, Tracking (particle physics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Vehicle dynamics, 020901 industrial engineering & automation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0203 mechanical engineering, 11. Sustainability, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Local search (optimization), Computer vision, Occupancy Grid, business.industry, Laser Scanner, 020302 automobile design & engineering, Object detection, Road Border Detection, SLAM, Perception, Artificial intelligence, DATMO, business, Host (network)
Abstract: Poster; International audience; In this paper we have presented a fast algorithm to detect road borders from laser data. Two local search windows, one on right side of the host vehicle and the other on left, are moved right and left respectively from the current position of vehicle in map. A score function is evaluated to know the presence or absence of the road border in current search window. We have used the detected road border information to reduce false alarms in our previous work on DATMO (detection and tracking of moving objects). We also show how these information can be used to infer drivable area and the presence of intersections on the road. Results on data sets obtained from real demonstrator vehicles show that this technique can be successfully applied in real time.
Published: 2012
Full Text: View/download PDF

10. Frontal object perception using radar and mono-vision

Author: J. Burlet, Trung-Dung Vu, Olivier Aycard, R. Omar Chavez-Garcia, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Computer science, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), law.invention, Lidar Signal Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], law, 11. Sustainability, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Integrated Safety Systems, Radar, Representation (mathematics), 050210 logistics & transportation, Radar tracker, business.industry, 05 social sciences, Object detection, Video tracking, Image, 020201 artificial intelligence & image processing, Artificial intelligence, Information fusion, Raw data, Software architecture, business
Abstract: Poster; International audience; In this paper, we detail a complete software architecture of a key task that an intelligent vehicle has to deal with: frontal object perception. This task is solved by processing raw data of a radar and a mono-camera to detect and track moving objects. Data sets obtained from highways, country roads and urban areas were used to test the proposed method. Several experiments were conducted to show that the proposed method obtains a better environment representation, i.e., reduces the false alarms and missed detections from individual sensor evidence.
Published: 2012
Full Text: View/download PDF

11. A generic architecture for dynamic outdoor environment

Author: Trung-Dung Vu, Thierry Fraichard, Olivier Aycard, Qadeer Baig, Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 020301 aerospace & aeronautics, 0209 industrial biotechnology, Computer science, business.industry, 02 engineering and technology, Reasoning, Simultaneous localization and mapping, Tracking (particle physics), Vehicle dynamics, 020901 industrial engineering & automation, Lidar, 0203 mechanical engineering, 11. Sustainability, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Perception, Artificial intelligence, Architecture, business, Sensor Data Processing, Sensor Data Modeling
Abstract: International audience; In this paper, we present a generic architecture for perception of an intelligent vehicle in dynamic outdoor environment. This architecture is composed of two levels: a first level dedicated to real-time local simultaneous localization and mapping (SLAM) and a second one is dedicated to detection and tracking of moving objects (DATMO). The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm on a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 and on a Volkswagen Demonstrator in the framework of the European Project Intersafe2.
Published: 2011
Full Text: View/download PDF

12. 3D Mapping of Outdoor Environment Using Clustering Techniques

Author: Manuel Yguel, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), University of Karlsruhe (TH), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Discretization, Computer science, 02 engineering and technology, Plan (drawing), computer.software_genre, Data modeling, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Learning, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Cluster analysis, Sensor Data Processing, business.industry, 020206 networking & telecommunications, Lidar, Data Modeling, Perception, Data mining, State (computer science), Artificial intelligence, business, computer
Abstract: International audience; The goal of mapping is to build a map of the environment using raw data provided by some sensors embedded on an intelligent vehicle. This map is used by an intelligent vehicle to have knowledge about its surrounding environment to better plan its future actions. In this paper, we present a method, based on occupancy grids [3], to map 3D environment. In this method, we discretize the environment in cells and the shape of each cell is approximated by one or several gaussians in order to achieve a balance between representational complexity and accuracy. Experimental results on 3D real outdoor data provided by a lidar are shown: a map of an urban environment is presented. Moreover a quantitative comparison of our method with state of the art methods is presented to show the interest of the method.
Published: 2011
Full Text: View/download PDF

13. Fusion Between Laser and Stereo Vision Data For Moving Objects Tracking In Intersection Like Scenario

Author: Thierry Fraichard, Qadeer Baig, Trung Dung Vu, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, Computer science, business.industry, Context (language use), 02 engineering and technology, Tracking (particle physics), Object (computer science), Sensor fusion, Object detection, Object-oriented design, 020901 industrial engineering & automation, Stereopsis, Intersection, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business
Abstract: International audience; Using multiple sensors in the context of environment perception for autonomous vehicles is quite common these days. Perceived data from these sensors can be fused at different levels like: before object detection, after object detection and finally after tracking the moving objects. In this paper we detail our object detection level fusion between laser and stereo vision sensors as opposed to pre-detection or track level fusion. We use the output of our laser processing to get a list of objects with position and dynamic properties for each object. Similarly we use the stereo vision output of another team which consists of a list of detected objects with position and classification properties for each object. We use Bayesian fusion technique on objects of these two lists to get a new list of fused objects. This fused list of objects is further used in tracking phase to track moving objects in an intersection like scenario. The results obtained on data sets of INTERSAFE-2 demonstrator vehicle show that this fusion has improved data association and track management steps.
Published: 2011
Full Text: View/download PDF

14. Intersection Safety using Lidar and Stereo Vision sensors

Author: Qadeer Baig, Siviu Bota, Fawzi Nashashibi, Trung-Dung Vu, Sergiu Nedevschi, Michel Parent, Olivier Aycard, Cosmin D. Pantilie, Paulo Resende, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Universitatea Tehnica din Cluj-Napoca (UTCN), Informatique, Mathématiques et Automatique pour la Route Automatisée (IMARA), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Robotique (CAOR), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), University of Grenoble - FRANCE, European Project: 223951,EC:FP7:ICT,FP7-ICT-2007-2,INTERSAFE-2(2008), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), MINES ParisTech - École nationale supérieure des mines de Paris, and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Three dimensional displays, Optical radar, 0203 mechanical engineering, Intersection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Radar imaging, Laser fusion, 0502 economics and business, 11. Sustainability, Computer vision, Image sensor, Risk management, 050210 logistics & transportation, Scope (project management), business.industry, Sensors, Tracking, 05 social sciences, 020302 automobile design & engineering, Vehicles, Stereo vision, Laser radar, Lidar, Stereopsis, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; In this paper, we describe our approach for intersection safety developed in the scope of the European project INTERSAFE-2. A complete solution for the safety problem including the tasks of perception and risk assessment using on-board lidar and stereo-vision sensors will be presented and interesting results are shown.
Published: 2011

15. Grid-based localization and local mapping with moving object detection and tracking

Author: Olivier Aycard, Trung-Dung Vu, J. Burlet, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Occupancy grid mapping, Laser scanning, Computer science, business.industry, 02 engineering and technology, Filter (signal processing), Simultaneous localization and mapping, Tracking (particle physics), Object detection, 020901 industrial engineering & automation, Odometry, Hardware and Architecture, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, A priori and a posteriori, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Software, Information Systems
Abstract: International audience; We present a real-time algorithm for simultaneous localization and local mapping (local SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner, short-range radars and odometry. To correct the vehicle odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After obtaining a good vehicle localization, the map surrounding of the vehicle is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Model (IMM) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.
Published: 2011
Full Text: View/download PDF

16. Low level data fusion of laser and monocular color camera using occupancy grid framework

Author: Olivier Aycard and Qadeer Baig
Subjects: Background subtraction, Occupancy grid mapping, Pixel, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Context (language use), Sensor fusion, computer.software_genre, Object detection, Grid computing, Computer vision, Artificial intelligence, Image sensor, business, computer
Abstract: In this paper we have developed a technique for low level data fusion between laser and monocular color camera using occupancy grid framework in the context of internal representation of external environment for object detection. Based on a small variant of background subtraction technique we construct an occupancy grid for camera and fuse it with the one constructed for laser to get a combined view. The results obtained using Cycab simulator prepared by INRIA show the effectiveness of our technique.
Published: 2010
Full Text: View/download PDF

17. Interacting multiple models based classification of moving objects

Author: Julien Burlet, Olivier Aycard, and Qadeer Baig
Subjects: Engineering, Matching (graph theory), business.industry, Video tracking, Process (computing), Trajectory, Computer vision, Filter (signal processing), Artificial intelligence, Simultaneous localization and mapping, business, Object (computer science), Object detection
Abstract: In this paper, we present an approach performing object behavior classification embedded in a complex and efficient perception method. This method, applied in dynamic outdoor environments using a moving vehicle equipped with a laser scanner, is composed of a local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO). While the SLAM is performed by an implementation of incremental scan matching method, the tracking if performed by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Models Filter (IMM). The classification process takes place in the filtering stage and is based on one of the key parameters of the IMM filter which is the Transition Probability Matrix (TPM) modeling objects motion transitions. It permits to automatically classify object behavior and to reuse the classification output to enhance the prediction step in the filtering process. The experimental results on datasets collected from a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 demonstrate the capacity of the proposed algorithm.
Published: 2010
Full Text: View/download PDF

18. Keynote lecture perception for intelligent vehicles

Author: Olivier Aycard
Subjects: business.industry, Computer science, media_common.quotation_subject, Mobile robot, Simultaneous localization and mapping, Tracking (particle physics), Object detection, Task (project management), Perception, Computer vision, Motion planning, Artificial intelligence, Advanced driver, business, media_common
Abstract: Perceiving or understanding the environment surrounding of a vehicle is a very important step in advanced driver assistant systems (ADAS) or autonomous vehicles. The task involves both Simultaneous Localization And Mapping (SLAM) and Detection And Tracking of Moving Objects (DATMO). While SLAM provides the vehicle with a map of static parts of the environment as well as its location in the map, DATMO allows the vehicle being aware of dynamic entities around, tracking them and predicting their future behaviors.
Published: 2010
Full Text: View/download PDF

19. Multiple pedestrian tracking using Viterbi data association

Author: Olivier Aycard and Asma Azim
Subjects: Computer science, business.industry, Association (object-oriented programming), Object (computer science), Sensor fusion, Viterbi algorithm, Object detection, symbols.namesake, symbols, Clutter, Computer vision, False alarm, Artificial intelligence, Hidden Markov model, business
Abstract: To address perception problems we must be able to track dynamic objects of the environment. An important issue of tracking is the association problem in which we have to associate each new observation with one existing object in the environment. This problem is complex: unfortunately, the number of observations generally does not correspond to the number of objects. Moreover, the number of objects is difficult to estimate since one object might be temporarily occluded or unobserved simply because objects can enter or go out of ranges of vehicle sensors. Moreover, the perception sensors or the object detection process might generate false alarm measurements. In this paper, we propose a new solution to solve the multiple objects tracking problem, using the Viterbi algorithm (VA) [2]. It is an established optimisation technique for discrete Markovian systems that has been extensively used in speech recognition. In this paper, we present an extension of VA to solve multiple objects tracking in clutter environment and show some experimental results on multiple pedestrian tracking and also some quantitative comparisons with MHT algorithms.
Published: 2010
Full Text: View/download PDF

20. Online localization and mapping with moving objects detection in dynamic outdoor environments

Author: Qadeer Baig, Trung-Dung Vu, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), IEEE, Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: 0209 industrial biotechnology, Computer science, business.industry, Mobile robot, 02 engineering and technology, Simultaneous localization and mapping, Object detection, Vehicle dynamics, 020901 industrial engineering & automation, Odometry, Video tracking, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], A priori and a posteriori, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Image sensor, business
Abstract: International audience; In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on dataset collected from INTERSAFE-2 demonstrator for typical scenario show the effectiveness of this technique.
Published: 2009
Full Text: View/download PDF

21. Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo

Author: Trung-Dung Vu, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: 050210 logistics & transportation, Laser scanning, business.industry, Computer science, 05 social sciences, Monte Carlo method, Markov process, 020302 automobile design & engineering, Markov chain Monte Carlo, 02 engineering and technology, Tracking (particle physics), Object detection, symbols.namesake, 0203 mechanical engineering, Sliding window protocol, 11. Sustainability, 0502 economics and business, symbols, Object model, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, business
Abstract: International audience; We present a method of simultaneous detection and tracking moving objects from a moving vehicle equipped with a single layer laser scanner. A model-based approach is introduced to interpret the laser measurement sequence by hypotheses of moving object trajectories over a sliding window of time. Knowledge of various aspects including object model, measurement model, motion model are integrated in one theoretically sound Bayesian framework. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to sample the solution space effectively to find the optimal solution. Experiments and results on real-life data of urban traffic show promising results.
Published: 2009
Full Text: View/download PDF

22. Grid-based localization and online mapping with moving objects detection and tracking: new results

Author: Trung-Dung Vu, J. Burlet, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 050210 logistics & transportation, 0209 industrial biotechnology, Computer science, business.industry, 05 social sciences, 02 engineering and technology, Filter (signal processing), Simultaneous localization and mapping, Sensor fusion, Remotely operated underwater vehicle, Object detection, Vehicle dynamics, Adaptive filter, 020901 industrial engineering & automation, Odometry, 11. Sustainability, 0502 economics and business, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Artificial intelligence, business
Abstract: International audience; In this paper, we present a real-time algorithm for local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a multiple hypothesis tracker (MHT) coupled with an adaptive IMM (interacting multiple models) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm on a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2.
Published: 2008
Full Text: View/download PDF

23. Update Policy of Dense Maps: efficient algorithms and sparse representation

Author: Manuel Yguel, Christian Laugier, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: 0209 industrial biotechnology, Moving parts, Occupancy grid mapping, Efficient algorithm, business.industry, Computer science, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.9: Robotics, 02 engineering and technology, Sparse approximation, RANSAC, 020901 industrial engineering & automation, Data association, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business
Abstract: International audience; Providing a robot with a fully detailed map is one appealing key for the Simultaneous Localisation and Mapping (SLAM) problem. It gives the robot a lot of hints to solve either the data association or the localisation problem itself. The more details are in the map, the more chances are that different places may appear differently, solving ambiguities. The more landmarks are used, the more accurate are the algorithms that solve the localisation problem since in a least square sense an approximation of the solution is more precise. Last, it helps a lot in the presence of a few dynamic objects because these moving parts of the environment remain marginal in the amount of data used to model the map and can thus be filtered out. For instance, the moving objects can be detected or cancelled in the localisation procedure by robust techniques using Monte-Carlo algorithms or RANSAC .
Published: 2007

24. Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments

Author: Olivier Aycard, Trung-Dung Vu, Nils Appenrodt, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Daimler Chrysler AG (Daimler), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Computer science, business.industry, Mobile robot, 02 engineering and technology, Simultaneous localization and mapping, Remotely operated underwater vehicle, Object detection, Vehicle dynamics, 020901 industrial engineering & automation, Odometry, Video tracking, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, A priori and a posteriori, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business
Abstract: International audience; In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.
Published: 2007

25. Adaptive Interacting Multiple Models applied on pedestrian tracking in car parks

Author: Christian Laugier, J. Burlet, Anne Spalanzani, and Olivier Aycard
Subjects: Engineering, business.industry, Component (UML), Filtering problem, A priori and a posteriori, Computer vision, State (computer science), Pedestrian, Artificial intelligence, Tracking (particle physics), Adaptation (computer science), business, Motion (physics)
Abstract: To address perception problems we must be able to track dynamics targets of the environment. An important issue of tracking is filtering problem in which estimates of the target's state are computed while observations are progressively received. This paper presents an adaptive Interacting Multiple Models (IMM) based filtering method. Interacting Multiple Models have been successfully applied to many applications as they allow, using several filters in parallel, to deal with the uncertainty on motion model, a critical component of filtering. Indeed targets can rapidly change their motion over a lapse of time. This is the case of pedestrians for which it is difficult to define an unique motion model which matches all their possible displacements. Nevertheless, the Transition Probability Matrix (TPM) which models the interaction between different filters in an IMM is in currently defined a priori or needs an important amount of tuning to be used efficiently. In this paper, we put forward a method which automatically adapts online the TPM. The TPM adaptation using on-line data significantly improves the effectiveness of IMM filtering and so better target estimates are obtained. To validate our work we applied our method to pedestrian tracking in car parks on a real platform.
Published: 2006
Full Text: View/download PDF

26. Efficient GPU-based Construction of Occupancy Girds Using several Laser Range-finders

Author: Manuel Yguel, Olivier Aycard, and Christian Laugier
Subjects: Occupancy grid mapping, business.industry, Computer science, OpenGL, computer.software_genre, Sensor fusion, Frame rate, Grid, Computational science, Computer graphics, Grid computing, Computer vision, Artificial intelligence, business, computer, Texture mapping
Abstract: Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new exact approach to this issue and we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. Therefore we introduce a calculus architecture to build occupancy grids with a graphical processor unit (GPU). Thus we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid. To validate our method, the results with GPU are compared to results obtained through the exact approach.
Published: 2006
Full Text: View/download PDF

27. Pedestrians Tracking Using Offboard Cameras

Author: Chiara Fulgenzi, J. Burlet, David Raulo, Anne Spalanzani, Olivier Aycard, Trung Dung Vu, and Manuel Yguel
Subjects: Engineering, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Context (language use), Pedestrian, Tracking (particle physics), Track (rail transport), Sensor fusion, Set (abstract data type), Software, Computer vision, Artificial intelligence, Image sensor, business
Abstract: In this paper, we detail the hardware and software perception system designed and developed to track pedestrians using a set of offboard cameras. It has been used in the context of vulnerable safety in a car park. This architecture is divided in two parts: a fusion part to fusion the data given by the set of offboard cameras and a tracking part to sequentially estimate the position of each pedestrian present in the environment and to determine the number of pedestrians. Finally, some experimental results are presented.
Published: 2006
Full Text: View/download PDF

28. Grid based fusion of off-board cameras

Author: David Raulo, Olivier Aycard, Christian Laugier, and Manuel Yguel
Subjects: Engineering, Occupancy grid mapping, business.industry, Detector, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Sensor model, Sensor fusion, Grid, Grid based, Object detection, Intersection, Computer vision, Artificial intelligence, business
Abstract: In this paper, we describe the perception system, based on a grid environment model, developed within the French project PUVAME. This system consists of several off-board cameras observing an intersection to detect objects (i.e. pedestrians, cyclists and vehicles). We present a generic and new method to design a sensor model for off-board cameras where each of the camera video stream is precessed independently by a dedicated detector. In addition, to obtain tolerance to miss detections and false alarms, we model the failure of each sensor. Then, we detail how to build an occupancy grid, fusing the information from the different cameras. Experimental results showing that our approach is well suited to build an environment model are provided.
Published: 2006
Full Text: View/download PDF

29. Pedestrian Tracking in Car Parks : An Adaptive Interacting Multiple Models Based Filtering Method

Author: J. Burlet, Christian Laugier, Anne Spalanzani, and Olivier Aycard
Subjects: Engineering, Mathematical model, business.industry, Component (UML), Filtering problem, A priori and a posteriori, Computer vision, State (computer science), Artificial intelligence, Tracking (particle physics), business, Adaptation (computer science), Motion (physics)
Abstract: To address perception problems we must be able to track dynamics targets of the environment. An important issue of tracking is filtering problem in which estimates of the target's state are computed while observations are progressively received. This paper presents an adaptive interacting multiple models (IMM) based filtering method. Interacting multiple models have been successfully applied to many applications as they allow, using several filters in parallel, to deal with the uncertainty on motion model, a critical component of filtering. Indeed targets can rapidly change their motion over a lapse of time. This is the case of pedestrians for which it is difficult to define a unique motion model which matches all their possible displacements. Nevertheless, the transition probability matrix (TPM) which models the interaction between different filters in an IMM is in currently defined a priori or needs an important amount of tuning to be used efficiently. In this paper, we put forward a method which automatically adapts online the TPM. The TPM adaptation using on-line data significantly improves the effectiveness of IMM filtering and so better target estimates are obtained. To validate our work we applied our method to pedestrian tracking in car parks on a real platform
Published: 2006
Full Text: View/download PDF

30. Efficient GPU-based Construction of Occupancy Grids Using several Laser Range-finders

Author: Olivier Aycard, Christian Laugier, Manuel Yguel, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: 0209 industrial biotechnology, Engineering, Occupancy grid mapping, business.industry, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Image processing, 02 engineering and technology, Grid, Sensor fusion, Computational science, Computer graphics, 020901 industrial engineering & automation, Exact algorithm, Sensor array, Control and Systems Engineering, 11. Sustainability, Automotive Engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Texture mapping
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2007/YAL07/ note: (Accepted) To be published; Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new efficient approach to this issue based upon a graphical processor unit (GPU). In that perspective, we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. We also present an exact algorithm in order to evaluate the accuracy of such a device, which is not precisely known due to the several approximations made by the hardware. To validate our method, the results with GPU are also compared to results obtained through the exact approach and the GPU precision is shown to be good enough for robotic applications. Therefore we describe a whole and general calculus architecture to build occupancy grids for any kind of range-finder with a graphical processor unit (GPU). And we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid.
Published: 2006

31. Grid Based Fusion & Tracking

Author: M. Yguel, Dung Vu, J. Burlet, David Raulo, Anne Spalanzani, Olivier Aycard, and Chiara Fulgenzi
Subjects: Engineering, business.industry, Position (vector), Computer vision, Context (language use), Tracking system, Artificial intelligence, Track (rail transport), Object (computer science), business, Tracking (particle physics), Sensor fusion, Object detection
Abstract: In this paper, we detail the perception system designed and developed in our group to track multi-objects. This system is divided in two parts: a fusion part to fusion the data given by different sensors and a tracking part to sequentially estimate the position of each object present in the environment and to determine the number of objects. We also present how this system has been used in the context of vulnerable safety in a car park. Finally, some experimental results are presented
Published: 2006
Full Text: View/download PDF

32. Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

Author: Richard Washington, Jean-François Mari, Olivier Aycard, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), NASA Ames Research Center (ARC), Automatic Programming and Decisional Systems in Robotics (SHARP), INRIA, Laugier, Christian, and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)
Subjects: FOS: Computer and information sciences, 0209 industrial biotechnology, Computer science, Computer Science - Artificial Intelligence, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], lcsh:TK7800-8360, 02 engineering and technology, lcsh:QA75.5-76.95, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Robotics, 020901 industrial engineering & automation, Artificial Intelligence, mobile robots, 0202 electrical engineering, electronic engineering, information engineering, Computer Science::Networking and Internet Architecture, sensor data interpretation, Computer vision, robot mobile, Hidden Markov model, interprétation de données, Interpretation (logic), Artificial neural network, business.industry, Maximum-entropy Markov model, lcsh:Electronics, hidden markov models, Pattern recognition, Mobile robot, Computer Science Applications, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Artificial Intelligence (cs.AI), Order (business), Pattern recognition (psychology), 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Electronic computers. Computer science, hmm, business, Software
Abstract: In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock., 2004
Published: 2003

33. Mobile robot localization in dynamic environments using places recognition

Author: Pierre Laroche, François Charpillet, and Olivier Aycard
Subjects: Engineering, Social robot, business.industry, Cognitive neuroscience of visual object recognition, Robot, Doors, Computer vision, Mobile robot, Motion planning, Pattern matching, Artificial intelligence, business, Mobile robot navigation
Abstract: We present a new method to localize a mobile robot in dynamic environments. This method is based on place recognition, and a match between places recognized and the sequence of places that the mobile robot is able to see during a run from an initial place to an ending place. Our method gives a coarse idea of the robot's position and orientation. Moreover, the robot can determine the actual state of places (i.e. open doors, closed doors).

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

33 results on '"Olivier Aycard"'

1. Visual-based Global Localization from Ceiling Images using Convolutional Neural Networks

2. A new tool to initialize global localization for a mobile robot

3. An Evidential Filter for Indoor Navigation of a Mobile Robot in Dynamic Environment

4. Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

5. Fusion at Detection Level for Frontal Object Perception

6. Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

7. Layer-based supervised classification of moving objects in outdoor dynamic environment using 3D laser scanner

8. Detection, classification and tracking of moving objects in a 3D environment

9. Improving moving objects tracking using road model for laser data

10. Frontal object perception using radar and mono-vision

11. A generic architecture for dynamic outdoor environment

12. 3D Mapping of Outdoor Environment Using Clustering Techniques

13. Fusion Between Laser and Stereo Vision Data For Moving Objects Tracking In Intersection Like Scenario

14. Intersection Safety using Lidar and Stereo Vision sensors

15. Grid-based localization and local mapping with moving object detection and tracking

16. Low level data fusion of laser and monocular color camera using occupancy grid framework

17. Interacting multiple models based classification of moving objects

18. Keynote lecture perception for intelligent vehicles

19. Multiple pedestrian tracking using Viterbi data association

20. Online localization and mapping with moving objects detection in dynamic outdoor environments

21. Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo

22. Grid-based localization and online mapping with moving objects detection and tracking: new results

23. Update Policy of Dense Maps: efficient algorithms and sparse representation

24. Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments

25. Adaptive Interacting Multiple Models applied on pedestrian tracking in car parks

26. Efficient GPU-based Construction of Occupancy Girds Using several Laser Range-finders

27. Pedestrians Tracking Using Offboard Cameras

28. Grid based fusion of off-board cameras

29. Pedestrian Tracking in Car Parks : An Adaptive Interacting Multiple Models Based Filtering Method

30. Efficient GPU-based Construction of Occupancy Grids Using several Laser Range-finders

31. Grid Based Fusion & Tracking

32. Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

33. Mobile robot localization in dynamic environments using places recognition

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Journal

Database

Publisher

33 results on '"Olivier Aycard"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources