Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

Visual Localization with Lines

Mobile robots must be able to derive their current location from sensor measurements in order to navigate fully autonomously. Positioning sensors like GPS output a global position but their precision is not sufficient for many applications; and indoors no GPS signal is received at all. Cameras provide information-rich data and are already used in many systems, e.g. for object detection and recognition. Therefore, this thesis investigates the possibility of additionally using cameras for localization. State-of-the-art methods are based on point observations but as man-made environments mostly consist of planar and linear structures which are perceived as lines, the focus in this thesis is on the use of image lines to derive the camera trajectory. To achieve this goal, multiple view geometry algorithms for line-based pose and structure estimation have to be developed. A prerequisite for these algorithms is that correspondences between line observations in multiple images which originate from the same spatial line are established. This thesis proposes a novel line matching algorithm for matching under small baseline motion which is designed with one-to-many matching in mind to tackle the issue of varying line segmentation. In contrast to other line matching solutions, the algorithm proposed leverages optical flow calculation and hence obviates the need for an expensive descriptor calculation. A two-view relative pose estimation algorithm is introduced which extracts the spatial line directions using parallel line clustering on the image lines in order to calculate the relative rotation. In lieu of the "Manhattan world" assumption, which is required by state-of-the-art methods, the approach proposed is less restrictive as it needs only lines of different directions; the angle between the directions is not relevant. In addition, the method proposed is in the order of one magnitude faster to compute. A novel line triangulation method is proposed to derive the scene structure from the images. The method is derived from the spatial transformation of Plücker lines and allows prior knowledge of the spatial line, like the precalculated directions from the parallel line clustering, to be integrated. The problem of degenerate configurations is analyzed, too, and a solution is developed which incorporates the optical flow vectors from the matching step as spatial points into the estimation. Lastly, all components are combined to a visual odometry pipeline for monocular cameras. The pipeline uses image-to-image motion estimation to calculate the camera trajectory. A scale adjustment based on the trifocal tensor is introduced which ensures the consistent scale of the trajectory. To increase the robustness, a sliding-window bundle adjustment is employed. All components and the visual odometry pipeline proposed are evaluated and compared to state-of-the-art methods on real world data of indoor and outdoor scenes. The evaluation shows that line-based visual localization is suitable to solve the localization task

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments

We propose a novel visual-inertial odometry approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on-the-fly, and their headings are gradually refined by the state estimator when new observations are coming. With fully exploration of structural lines that aligned with each local Manhattan worlds, our visual-inertial odometry method become more accurate and robust, as well as much more flexible to different kinds of complex man-made environments. Through extensive benchmark tests and real-world tests, the results show that the proposed approach outperforms existing visual-inertial systems in large-scale man-made environmentsComment: 15 pages,15 figure

Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments

Image-based estimation of camera motion, known as visual odometry (VO), plays a very important role in many robotic applications such as control and navigation of unmanned mobile robots, especially when no external navigation reference signal is available. The core problem of VO is the estimation of the camera’s ego-motion (i.e. tracking) either between successive frames, namely relative pose estimation, or with respect to a global map, namely absolute pose estimation. This thesis aims to develop efficient, accurate and robust VO solutions by taking advantage of structural regularities in man-made environments, such as piece-wise planar structures, Manhattan World and more generally, contours and edges. Furthermore, to handle challenging scenarios that are beyond the limits of classical sensor based VO solutions, we investigate a recently emerging sensor — the event camera and study on event-based mapping — one of the key problems in the event-based VO/SLAM. The main achievements are summarized as follows. First, we revisit an old topic on relative pose estimation: accurately and robustly estimating the fundamental matrix given a collection of independently estimated homograhies. Three classical methods are reviewed and then we show a simple but nontrivial two-step normalization within the direct linear method that achieves similar performance to the less attractive and more computationally intensive hallucinated points based method. Second, an efficient 3D rotation estimation algorithm for depth cameras in piece-wise planar environments is presented. It shows that by using surface normal vectors as an input, planar modes in the corresponding density distribution function can be discovered and continuously tracked using efficient non-parametric estimation techniques. The relative rotation can be estimated by registering entire bundles of planar modes by using robust L1-norm minimization. Third, an efficient alternative to the iterative closest point algorithm for real-time tracking of modern depth cameras in ManhattanWorlds is developed. We exploit the common orthogonal structure of man-made environments in order to decouple the estimation of the rotation and the three degrees of freedom of the translation. The derived camera orientation is absolute and thus free of long-term drift, which in turn benefits the accuracy of the translation estimation as well. Fourth, we look into a more general structural regularity—edges. A real-time VO system that uses Canny edges is proposed for RGB-D cameras. Two novel alternatives to classical distance transforms are developed with great properties that significantly improve the classical Euclidean distance field based methods in terms of efficiency, accuracy and robustness. Finally, to deal with challenging scenarios that go beyond what standard RGB/RGB-D cameras can handle, we investigate the recently emerging event camera and focus on the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping

Visual Odometry Estimation Using Selective Features

The rapid growth in computational power and technology has enabled the automotive industry to do extensive research into autonomous vehicles. So called self- driven cars are seen everywhere, being developed from many companies like, Google, Mercedes Benz, Delphi, Tesla, Uber and many others. One of the challenging tasks for these vehicles is to track incremental motion in runtime and to analyze surroundings for accurate localization. This crucial information is used by many internal systems like active suspension control, autonomous steering, lane change assist and many such applications. All these systems rely on incremental motion to infer logical conclusions. Measurement of incremental change in pose or perspective, in other words, changes in motion, measured using visual only information is called Visual Odometry. This thesis proposes an approach to solve the Visual Odometry problem by using stereo-camera vision to incrementally estimate the pose of a vehicle by examining changes that motion induces on the background in the frame captured from stereo cameras. The approach in this thesis research uses a selective feature based motion tracking method to track the motion of the vehicle by analyzing the motion of its static surroundings and discarding the motion induced by dynamic background (outliers). The proposed approach considers that the surrounding may have moving objects like a truck, a car or a pedestrian body which has its own motion which may be different with respect to the vehicle. Use of stereo camera adds depth information which provides more crucial information necessary for detecting and rejecting outliers. Refining the interest point location using sinusoidal interpolation further increases the accuracy of the motion estimation results. The results show that by using a process that chooses features only on the static background and by tracking these features accurately, robust semantic information can be obtained