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Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this paper, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parametrize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques, and substantially outperforms them on problems that involve many workspace constraints., Comment: 20 pages, 14 figures

The article solves the problem of more efficient and economical training of combat crews on short range air defense systems. The training so far is based on the use of conventional means, which require engagement of a large number of people, expensive equipment, long-term planning and spending a lot of time and space. The use of unmanned aerial vehicles - drones, greatly saves all these resources. Our mathematical model, using the methods of multicriteria decision-making - Analytical Hierarchical Processes (AHP) and optimization - The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), determines the weighting coefficients and then ranks alternatives or, the ranking for drone selection, which would replace classical means for training and co- aching so meeting all parameters and characteristics of the training itself and the training equipment. The methods first prioritize the selection criteria, and then, based on their importance, concretize the solution among the offered alternatives.

This work proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post- optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state of the art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post- optimization and smoothing required. HE* is a two- stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.

The complex relationship between linear energy transfer (LET) and cellular response to radiation is not yet fully elucidated. To better characterize DNA damage after irradiations with therapeutic protons, we monitored formation and disappearance of DNA double-strand breaks (DNA DSB) as a function of LET and time. Comparisons with conventional γ-rays and high LET carbon ions were also performed.

We present a novel approach to automatic indoor scene reconstruction from RGB-D images acquired from a single viewpoint using active vision. The proposed method is designed to select the next view with sufficient information for reliable registration. The next view is selected based on the percentage of unexplored scene regions captured inside the field of view and the information content in the overlapping region between the image acquired from the next view and one of the previously acquired images. It is required that this overlapping region contains surfaces with different orientations, whose alignment provides a reliable estimation of the relative camera orientation. The point correspondences between views are established using the assumption of fixed viewpoint, imprecise information about relative view orientation and local surface normal, without need for features based on texture or distinctive local shape. After completing a scan, the 3D scene model is constructed by performing registration of the acquired depth images. Two algorithms are considered for that purpose: a point-to-plane ICP with point weighting based on the properties of the measurement noise and TEASER++. The proposed method is tested on the synthetic dataset Replica.

Analysis of elimination of four human radioresistant malignant cell lines to mono-energetic and non mono-energetic incoming carbon ion beams, characterized by different linear energy transfer (LET) qualities is performed. Comparisons with protons from the middle of the therapeutic spread out Bragg peak (SOBP) and reference γ-rays are also included.HTB140 cells were irradiated at five positions, with different LET, along the 62 MeV carbon pristine Bragg peak. To provide reliable reproducibility of irradiations at INFN-LNS, as the carbon Bragg peak is very narrow, precise positioning of samples for desired LET value is complicated. The peak was slightly widened using two ripple filters. After defining irradiation position and LET at the peak itself where cell killing is almost the highest, irradiation position with the same LET value was found within somewhat broadened peak. HTB140, MCF-7, HTB177 and CRL5876 cells were irradiated at the two described positions. Additionally, irradiations in the middle of 62 MeV proton SOBP and reference γ-rays were performed. Doses ranged from 0.5 to 16 Gy. Cell survival and corresponding radiobiological parameters were assessed seven days after irradiations.When moving irradiation position along the carbon Bragg curve, LET rises from 85 to 747 keV/µm, while surviving fraction at 2 Gy (SF2) for HTB140 cells, falls from 0.72 to 0.57 further rising to 0.73 on the distal fall-off part of the curve. Improved cell radiosensitivity is seen for the doses below 4 Gy. Relative biological effectiveness (RBE) increases from 4.56 to 7.69 and drops to 4.23. Almost the highest cell killing LET, being ∼200 keV/µm, is used to irradiate HTB140, MCF-7, HTB177 and CRL5876 cells within the pristine and slightly broadened Bragg peak. After irradiations with protons of the mid SOBP, carbon ions of the pristine and slightly widened Bragg peak RBE ranges for HTB140 cells from 2.08, 4.81 to 7.06, for MCF-7 from 1.70, 3.28 to 4.17, for HTB177 from 1.98, 4.18 to 5.08 and for CRL5876 from 1.33, 2.57 to 3.51.Significant elimination of HTB140 cells is observed along the carbon Bragg curve. The highest one is achieved by LET that is at the level of already reported. For the same LET, mono-energetic carbon ions provide higher cell elimination than the non mono-energetic. For all cell lines, both carbon ion beams, more the monoenergetic one, express stronger killing rate than protons and especially γ-rays.

Event cameras are biologically inspired sensors that asynchronously detect brightness changes in the scene independently for each pixel. Their output is a stream of events which is reported with a low latency and high temporal resolution of a microsecond, making them superior to standard cameras in highly dynamic scenarios when they are sensitive to motion blur. Event cameras can be used in a wide range of applications, one of them being depth estimation, in both stereo and monocular settings. However, most known event-based depth estimation methods yield sparse depth maps due to the nature of the sparse event stream. We present a novel method that fuses information from both events and standard frames, as well as odometry, to exploit the advantages of both sensors. We propose to estimate dense disparity from standard frames at the point of their availability, predict the disparity using odometry information, and track the disparity asynchronously using optical flow of events between the standard frames. We present the performance of the method through several experiments in various setups, including synthetic data, KITTI dataset enhanced with events, MVSEC dataset, as well as our own stereo event camera recordings.

In this paper the criteria for selection of air traffic control (ATC) radar position that provide successfully fulfilled role of radar in air traffic management are determined and evaluated. Using the questionnaire, experts determined the initial criteria for selecting the radar position. Furthermore, the hybridized DEMATEL-AHP-TOPSIS model was modified by using the interval type-2 fuzzy sets (IT2FS). Less important criteria were eliminated by using the IT2FS-DEMATEL method, the prioritization of the final criteria was carried out by using the IT2FS-AHP method and a multi-criteria decision making model was proposed. Of the four ATC radar positions offered, the optimal position was selected by using the IT2FS-TOPSIS method. Validation of model was carried out by using Fuzzy and the IT2FS modified methods: TOPSIS, COPRAS and MABAC. A sensitivity analysis was carried out through 36 scenarios of changes in the criteria’s weights.

Physical training is an integral part of the overall training in the Serbian Armed Forces. It is a basis of combat preparedness of every soldier, hence it has to be an inherent part of their lives. The main idea of the current research is to examine weather high intensity interval exercises have a positive influence on the functional and motor abilities of the members of the Serbian Armed Forces. Repetitive strength, as one of the tested abilities, was significantly better in the experimental group of participants, which implies that applied interval exercise had improved the results of repetitive strength tests. These findings are in line with previous studies. Furthermore, it has been found that the experimental program of exercise did not influence functional abilities, i.e. there is no differences in aerobic fitness between the experimental and the control group. The experimental program should serve as an idea and basis for the finding, creation and designing of new ways of exercise within physical training at all levels in the system such is the Serbian Armed Forces.

Modern logistic solutions for large warehouses consist of a fleet of robots that transfer goods, move racks, and perform other physically difficult and repetitive tasks. The shopfloor is usually enclosed with a safety fence and if a human needs to enter the warehouse all the robots are stopped, as opposed to only the ones in the most immediate vicinity of the human, thus significantly limiting the warehouse efficiency. To tackle this challenge, an integrated safety system is needed with human localization as one of its essential components. In this paper, we propose a novel human localization method for robotized warehouses that is based on a suite of wearable visual sensors installed on a vest worn by humans. The proposed method does not require any modifications of the warehouse environment and relies on the already existing infrastructure. Specifically, we estimate the human location by fusing stereo visual-inertial odometry data and distances to the known absolute poses of the detected ground-markers which robots use for their localization. Fusion is performed by building a pose graph, where we treat estimated human poses relative to markers as graph nodes and odometry estimates as graph edges. We conducted extensive laboratory and warehouse facility experiments, where we tested the reliability and accuracy of the proposed method and compared its performance to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results indicate that our method can track absolute position in real-time and has competitive accuracy with respect to ORB-SLAM2, while ensuring higher localization reliability when faced with structural changes in the environment. Furthermore, we provide publicly the experimental datasets to the research community.

Depth estimation is an important task in robotics and autonomous driving. By estimating depth and relying only on a single camera, it is no longer necessary to add and calibrate additional sensors – usually a second camera. However, such an approach requires training on extensive datasets and obtaining real-world datasets is time consuming and costly. Given that, using photorealistic simulators can be beneficial, since a multitude of various scenes can be created. In this paper we present an approach to training a deep neural network based on the ResNet architecture for estimating depth from a single camera. We target road vehicle scenes and use the CARLA simulator. We evaluate the trained network on the real-world KITTI dataset images and in the CARLA simulator. In the simulated experiments, we compare the performance with respect to the changes in camera intrinsic and extrinsic calibration parameters with respect to the ego vehicle frame.

Teleoperation is an essential component of a robotic system utilized for execution of various remote tasks, e.g. telepresence, reconnaissance or search and rescue. In this paper we investigate the usage of User Datagram Protocol (UDP) for two-way communication between an Android mobile device and Robot Operating System (ROS). UDP offers simplified and connectionless communication, which allows high transmission rates and enables real-time applications. We develop an Android application which turns omnipresent Android devices into cheap and intuitive robot teleoperation interfaces. It enables interpreting user's desired movement choices into corresponding datagrams, which are then sent to the robot via wireless network. As a case study, we implement a solution for controlling the Turtlebot3 differential drive mobile robot within ROS. We examine the utility of UDP-based teleoperation in both simulation and real-life environment. We complement the paper with modular open-source implementations of our Android application and a ROS package featuring the Turtlebot3 controller.

Simultaneous localization and mapping (SLAM) is an important tool that enables autonomous navigation of mobile robots through unknown environments. As the name SLAM suggests, it is important to obtain a correct representation of the environment and estimate a correct trajectory of the robot poses in the map. Dominant state-of-the-art approaches solve the pose estimation problem using graph optimization techniques based on the least squares minimization method. Among the most popular approaches are libraries such as g$^2$o, Ceres, GTSAM and SE-Sync. The aim of this paper is to describe these approaches in a unified manner and to evaluate them on an array of publicly available synthetic and real-world pose graph datasets. In the evaluation experiments, the computation time and the value of the objective function of the four optimization libraries are analyzed.

This paper describes the proces of electric arc welding in the zone of protective gas, with an emphasis on transmitting metal dropelts via a short ciruit.Parameters of the welding proces and of an electric arc are described, and a mathemacal model for the MIG system is sugessted. Model simulation is completed used Matlab/Simulink package. The proces is simulated using real parameters in order for the output values of the process to corespond to real values obtained by the means of mesasurement.

As robots are progressing towards being ubiquitous and an indispensable part of our everyday environments, such as home, offices, healthcare, education, and manufacturing shop floors, efficient and safe collaboration and cohabitation become imperative. Given that, such environments could benefit greatly from accurate human action prediction. In addition to being accurate, human action prediction should be computationally efficient, in order to ensure a timely reaction, and capable of dealing with changing environments, since unstructured interaction and collaboration with humans usually do not assume static conditions. In this paper, we propose a model for human action prediction based on motion cues and gaze using shared-weight Long Short-Term Memory networks (LSTMs) and feature dimensionality reduction. LSTMs have proven to be a powerful tool in processing time series data, especially when dealing with long-term dependencies ; however, to maximize their performance, LSTM networks should be fed with informative and quality inputs. Given that, in this paper, we furthermore conducted an extensive input feature analysis based on (i) signal correlation and their strength to act as stand-alone predictors, and (ii) a multilayer perceptron inspired by the autoencoder architecture. We validated the proposed model on a publicly available MoGaze1 dataset for human action prediction, as well as on a smaller dataset recorded in our laboratory. Our model outperformed alternatives, such as recurrent neural networks, a fully connected LSTM network, and the strongest stand-alone signals (baselines), and can run in real-time on a standard laptop CPU. Since eye gaze might not always be available in a real-world scenario, we have implemented and tested a multi- layer perceptron for gaze estimation from more easily obtainable motion cues, such as head orientation and hand position. The estimated gaze signal can be utilized during inference of our LSTM-based model, thus making our action prediction pipeline suitable for real-time practical applications

Mobile robot localization is an important module for mobile robot navigation. Known mobile robot pose is needed for path-planning and task planning purposes. Localization can be relative or absolute. This chapter will describe several methods that combine relative and absolute mobile robot localization assuming that initial pose is given also known as self-localization method. Self-localization means that after the mobile robot receives its initial pose it can determine its pose using only onboard sensor measurements and respective environment model during its movement. As a case study to demostrate proposed concept following onboard sensors are used: encoders for odometry, sonar sensor and monocular-camera. Sonar sensor is used combined with an occupancy grid model and Extended or Unscented Kalman filter for sensor fusion. Mono-camera is used combined with a 3D world model and Extended Kalman filter for sensor fusion. Implemented self-localizations methods are experimentally evaluated using a Pioneer 2DX/3DX mobile robot in a corridor like environment. Obtained results are compared regarding to obtained pose estimation accuracy, memory consumption and computational complexity.

Rad se sastoji iz osam konceptualnih delova u kojima se razmatra odbrana od poplava na teritoriji opštine Bela Crkva, kao i buduće preventivne mere nastanka poplava na ovom području. Poplave su prirodna pojava koju nije moguće sprečiti, međutim čovek svojim aktivnostima ponekad itekako doprinosi njihovoj pojavi. Zbog opasnosti koje poplave nose sa sobom i štete koju mogu da učine stanovništvu i objektima na području koje zahvate, odbrana od poplava je preko potrebna, posebno u naseljenim mestima. Akcenat u master radu stavljen je upravo na načine odbrane od poplava koji postoje na teritoriji opštine Bela Crkva, kao i načine za unapređenje ove odbrane i sprečavanja pojave poplava na ovoj teritoriji.

This paper aims to determine a way how to assess the impact of a strategic partnership on bilateralism and check that impact. In that sense, the strength of strategic partnership influence on bilateralism areas has been probed. In order to check the impact the research has been conducted on the case of the strategic partnership between the Republic of Serbia and six countries. Under those circumstances, different methods have been applied: expert-based evaluation method, DEMATEL (Decision Making Trial and Evaluation Laboratory Method) and statistical method. Some data has been collected by a survey questionnaire on 15 specialists, but the main data is accurate data from statistical yearbooks. There are two general results of this research. One of them is the model of the assessment of the strategic partnership impact on bilateralism. The second result is the confirmation of the strategic partnership impact on bilateralism. That impact varies from zero to perfect impact depending on a strategic partner and bilateralism area. The original purpose of the model is to assess bilateralism and the strategic partnership impact. In addition, the model is multipurpose and can be used to assess different relationships in public and private sector. The research results can help to understand cooperation in general and relationship between different areas. This study extends prior researches by examining the comprehensive process of bilateralism scope assessment. On the basis of the research results, implementation of the comprehensive process has been confirmed and improved.

As robots are becoming more and more ubiquitous in human environments, it will be necessary for robotic systems to better under- stand and predict human actions. However, this is not an easy task, at times not even for us humans, but based on a relatively structured set of possible actions, appropriate cues, and the right model, this problem can be computationally tackled. In this paper, we propose to use an ensemble of long-short term memory (LSTM) networks for human action prediction. To train and evaluate models, we used the MoGaze 1 dataset – currently the most comprehensive dataset capturing poses of human joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and selected a reduced set of cues for this task. Our model can predict (i) which of the labeled objects the human is going to grasp, and (ii) which of the macro locations the human is going to visit (such as table or shelf). We have exhaustively evaluated the proposed method and compared it to individual cue baselines. The results suggest that our LSTM model slightly outperforms the gaze baseline in single object picking accuracy, but achieves better accuracy in macro object prediction. Furthermore, we have also analyzed the prediction accuracy when the gaze is not used, and in this case, the LSTM model considerably outperformed the best single cue baseline.

International audience; Purpose: The complex relationship between linear energy transfer (LET) and cellular response to radiation is not yet fully elucidated. To better characterize DNA damage after irradiations with therapeutic protons, we monitored formation and disappearance of DNA double strand breaks (DNA DSB) as a function of LET and time. Comparisons with conventional γ-rays and high LET carbon ions were also performed.Materials and methods: In the present work we performed immunofluorescence-based assay to determine the amount of DNA DSB induced by different LET values along the 62 MeV therapeutic proton Spread out Bragg peak (SOBP) in three cancer cell lines, i.e., HTB140 melanoma, MCF-7 breast adenocarcinoma and HTB177 non-small lung cancer cells. Time dependence of foci formation was followed as well. To determine irradiation positions, corresponding to the desired LET values, numerical simulations were carried out using Geant4 toolkit. We compared γ-H2AX foci persistence after irradiations with protons to that of γ-rays and carbon ions.Results: With the rise of LET values along the therapeutic proton SOBP, the increase of γ-H2AX foci number is detected in the three cell lines up to the distal end of the SOBP, while there is a decrease on its distal fall-off part. With the prolonged incubation time the number of foci gradually drops tending to attain the residual level. For the maximum number of DNA DSB irradiation with protons attain higher level than that of γ-rays. Carbon ions produce more DNA DSB than protons but not substantially. The number of residual foci produced by γ-rays is significantly lower than that of protons and particularly carbon ions. Carbon ions do not produce considerably higher number of foci than protons, as it could be expected due to their physical properties.Conclusions: In situ visualization of γ-H2AX foci reveal creation of more lesions in the three cell lines by clinically relevant proton SOBP than γ-rays. The lack of significant differences in the number of γ-H2AX foci between the proton and carbon ion-irradiated samples suggests an increased complexity of DNA lesions and slower repair kinetics after carbon ions compared to protons. For all three irradiation types, there is no major difference between the three cell lines shortly after irradiations, while later on, the formation of residual foci starts to express the inherent nature of tested cells, therefore increasing discrepancy between them.

CATANA (Centro di AdroTerapia ed Applicazioni Nucleari Avanzate) was the first Italian protontherapy facility dedicated to the treatment of ocular neoplastic pathologies. It is in operation at the LNS Laboratories of the Italian Institute for Nuclear Physics (INFN-LNS) and to date, 500 patients have been successfully treated. Even though proton therapy has demonstrated success in clinical settings, there is still a need for more accurate models because they are crucial for the estimation of clinically relevant RBE values. Since RBE can vary depending on several physical and biological parameters, there is a clear need for more experimental data to generate predictions. Establishing a database of cell survival experiments is therefore useful to accurately predict the effects of irradiations on both cancerous and normal tissue. The main aim of this work was to compare RBE values obtained from in-vitro experimental data with predictions made by the LEM II (Local Effect Model), Monte Carlo approaches, and semi-empirical models based on LET experimental measurements. For this purpose, the 92.1 uveal melanoma and ARPE-19 cells derived from normal retinal pigmented epithelium were selected and irradiated in the middle of clinical SOBP of the CATANA proton therapy facility. The remarkable results show the potentiality of using microdosimetric spectrum, Monte Carlo simulations and LEM model to predict not only the RBE but also the survival curves.

Articulated robots such as manipulators increasingly must operate in uncertain and dynamic environments where interaction (with human coworkers, for example) is necessary. In these situations, the capacity to quickly adapt to unexpected changes in operational space constraints is essential. At certain points in a manipulator's configuration space, termed singularities, the robot loses one or more degrees of freedom (DoF) and is unable to move in specific operational space directions. The inability to move in arbitrary directions in operational space compromises adaptivity and, potentially, safety. We introduce a geometry-aware singularity index, defined using a Riemannian metric on the manifold of symmetric positive definite matrices, to provide a measure of proximity to singular configurations. We demonstrate that our index avoids some of the failure modes and difficulties inherent to other common indices. Further, we show that this index can be differentiated easily, making it compatible with local optimization approaches used for operational space control. Our experimental results establish that, for reaching and path following tasks, optimization based on our index outperforms a common manipulability maximization technique and ensures singularity-robust motions., Comment: 12 pages, 12 figures

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

Robust and reliable perception of autonomous systems often relies on fusion of heterogeneous sensors, which poses great challenges for multisensor calibration. In this article, we propose a method for multisensor calibration based on Gaussian processes (GPs) estimated moving target trajectories, resulting with spatiotemporal calibration. Unlike competing approaches, the proposed method is characterized by the following: first, joint multisensor on-manifold spatiotemporal optimization framework, second, batch state estimation and interpolation using GPs, and, third, computational efficiency with O(n) complexity. It only re-quires that all sensors can track the same target. The method is validated in simulation and real-world experiments on the following five different multisensor setups: first, hardware triggered stereo camera, second, camera and motion capture system, third, camera and automotive radar, fourth, camera and rotating 3-D lidar, and, fifth, camera, 3-D lidar, and the motion capture system. The method estimates time delays with the accuracy up to a fraction of the fastest sensor sampling time, outperforming a state-of-the-art ego-motion method. Furthermore, this article is complemented by an open-source toolbox implementing the calibration method available at bitbucket.org/unizg-fer-lamor/calirad.

Riemannian manifolds are attracting much interest in various technical disciplines, since generated data can often be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of such manifolds, usual Euclidean methods yield inferior results, thus motivating development of tools adapted or specially tailored to the true underlying geometry. In this letter we propose a method for tracking multiple targets residing on smooth manifolds via probabilistic data association. By using tools of differential geometry, such as exponential and logarithmic mapping along with the parallel transport, we extend the Euclidean multi-target tracking techniques based on probabilistic data association to systems constrained to a Riemannian manifold. The performance of the proposed method was extensively tested in experiments simulating multi-target tracking on unit hyperspheres, where we compared our approach to the von Mises-Fisher and the Kalman filters in the embedding space that projects the estimated state back to the manifold. Obtained results show that the proposed method outperforms the competitive trackers in the optimal sub-pattern assignment metric for all the tested hypersphere dimensions. Although our use case geometry is that of a unit hypersphere, our approach is by no means limited to it and can be applied to any Riemannian manifold with closed-form expressions for exponential/logarithmic maps and parallel transport along the geodesic curve. The paper code is publicly available 1 .

Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigid-body transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors - however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments., Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'21), Xi'an, China, May 30-Jun. 5, 2021

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on the MVSEC sequences, while on the DSEC dataset ESVO, unlike our method, was unable to handle outdoor driving scenario with default parameters. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization., Comment: Submitted to Accepted to European Conference on Mobile Robots (ECMR) 2021

In this paper we consider an aerial vehicle transporting a suspended payload and propose an Extended Kalman filter for payload state estimation. The filter is based on derived system dynamics and relies solely on onboard IMU measurements. Effectiveness of the method is verified in numerical simulations and experimentally.