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In this paper, we present a system for estimating the trajectory of a moving RGB-D camera with applications to building maps of large indoor environments. Unlike the current most researches, we propose a 'feature model' based RGB-D visual odometry system for a computationally-constrained mobile platform, where the 'feature model' is persistent and dynamically updated from new observations using a Kalman filter. In this paper, we firstly propose a mixture of Gaussians model for the depth random noise estimation, which is used to describe the spatial uncertainty of the feature point cloud. Besides, we also introduce a general depth calibration method to remove systematic errors in the depth readings of the RGB-D camera. We provide comprehensive theoretical and experimental analysis to demonstrate that our model based iterative-closest-point (ICP) algorithm can achieve much higher localization accuracy compared to the conventional ICP. The visual odometry runs at frequencies of 30 Hz or higher, on VGA images, in a single thread on a desktop CPU with no GPU acceleration required. Finally, we examine the problem of place recognition from RGB-D images, in order to form a pose-graph SLAM approach to refining the trajectory and closing loops. We evaluate the effectiveness of the system on using publicly available datasets with ground-truth data. The entire system is available for free and open-source online. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) for smartphones has matured from a technology for earlier adopters, available only on select high-end phones, to one that is truly available to the general public. One of the key breakthroughs has been in low-compute methods for six degree of freedom (6DoF) tracking on phones using only the existing hardware (camera and inertial sensors). 6DoF tracking is the cornerstone of smartphone AR allowing virtual content to be precisely locked on top of the real world. However, to really give users the impression of believable AR, one requires mobile depth. Without depth, even simple effects such as a virtual object being correctly occluded by the real-world is impossible. However, requiring a mobile depth sensor would severely restrict the access to such features. In this article, we provide a novel pipeline for mobile depth that supports a wide array of mobile phones, and uses only the existing monocular color sensor. Through several technical contributions, we provide the ability to compute low latency dense depth maps using only a single CPU core of a wide range of (medium-high) mobile phones. We demonstrate the capabilities of our approach on high-level AR applications including real-time navigation and shopping. [ABSTRACT FROM AUTHOR]

Google's Project Tango has made integrated depth sensing and onboard visual-intertial odometry available to mobile devices such as phones and tablets. In this work, we explore the problem of large-scale, real-time 3D reconstruction on a mobile devices of this type. Solving this problem is a necessary prerequisite for many indoor applications, including navigation, augmented reality and building scanning. The main challenges include dealing with noisy and low-frequency depth data and managing limited computational and memory resources. State of the art approaches in large-scale dense reconstruction require large amounts of memory and high-performance GPU computing. Other existing 3D reconstruction approaches on mobile devices either only build a sparse reconstruction, offload their computation to other devices, or require long post-processing to extract the geometric mesh. In contrast, we can reconstruct and render a global mesh on the fly, using only the mobile device's CPU, in very large (300 m $$^2$$ ) scenes, at a resolutions of 2-3 cm. To achieve this, we divide the scene into spatial volumes indexed by a hash map. Each volume contains the truncated signed distance function for that area of space, as well as the mesh segment derived from the distance function. This approach allows us to focus computational and memory resources only in areas of the scene which are currently observed, as well as leverage parallelization techniques for multi-core processing. Furthermore, we describe an on-device post-processing method for fusing datasets from multiple, independent trials, in order to improve the quality and coverage of the reconstruction. We discuss how the particularities of the devices impact our algorithm and implementation decisions. Finally, we provide both qualitative and quantitative results on publicly available RGB-D datasets, and on datasets collected in real-time from two devices. [ABSTRACT FROM AUTHOR]

Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm. [ABSTRACT FROM AUTHOR]

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter. [ABSTRACT FROM AUTHOR]

This paper presents an open-source indoor navigation system for quadrotor micro aerial vehicles (MAVs), implemented in the ROS framework. The system requires a minimal set of sensors including a planar laser range-finder and an inertial measurement unit. We address the issues of autonomous control, state estimation, path-planning, and teleoperation, and provide interfaces that allow the system to seamlessly integrate with existing ROS navigation tools for 2D SLAM and 3D mapping. All components run in real time onboard the MAV, with state estimation and control operating at 1 kHz. A major focus in our work is modularity and abstraction, allowing the system to be both flexible and hardware-independent. All the software and hardware components which we have developed, as well as documentation and test data, are available online. [ABSTRACT FROM AUTHOR]