Visual odometry errors and fault distinction for integrity monitoring
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ORIGINAL PAPER
Visual odometry errors and fault distinction for integrity monitoring Yuanwen Fu1 · Shizhuang Wang1 · Yawei Zhai1 · Xingqun Zhan1 Received: 19 July 2020 / Revised: 21 August 2020 / Accepted: 1 September 2020 © Shanghai Jiao Tong University 2020
Abstract Visual odometry (VO) has been widely used for many purposes in the past decade. However, the three assumptions of VO are not often met in the reality, and therefore, the uncertainty of VO output (the pose of agent) should be estimated for safety’s sake, which can be done suitably by the integrity monitoring. To construct the integrity monitoring framework of VO, the first step is to establish the model of errors of measurements and calculate the fault rate, which has not been found in the literature to our knowledge. In response, this paper aims at establishing the model of errors of spatial points and calculating the fault rate in the stereo VO based on the feature point method. In this work, we describe the principle of stereo VO based on the feature point method in a deep and comprehensive way. The errors and faults of spatial points in stereo VO are defined, distinguished and classified in detail. The error propagation from pixel to spatial point is deduced, and the model of errors of spatial points is constructed. The KITTI odometry dataset is employed to evaluate the fault rate and standard deviation of errors of spatial points. And multiple sets of sensitive analyses are carried out to address the impact of RANdom SAmple Consensus (RANSAC) threshold, RANSAC iterations and operational scenario on spatial point error and fault rate. This paper could be a reference for constructing the integrity monitoring framework of stereo VO based on the feature point method. Keywords Stereo vision · Localization · Visual odometry · Error model · Fault detection · Integrity monitoring
1 Introduction Due to the limitations of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), visual odometry (VO) [3, 8, 11, 12] has been proposed with great complementary significance, which is a technique that estimates incrementally the ego-motion of camera or agent only with the visual input. VO has a wide range of applications in recent years, especially in mobile robot systems, such as space rovers, aerial drones, cleaning robots and even autonomous driving. VO is reliable and accurate under favorable conditions, such as when there is sufficient illumination and texture to establish correspondence, sufficient overlap between consecutive frames, and when the majority of the visible scene is static [20]. However, these conditions are not always met in the reality, and therefore, the uncertainty of VO output (the pose of vehicle) should be estimated for sake of safety. Integrity monitoring is a good fit for this job, which is a technique that can evaluate the integrity of a navi-
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Xingqun Zhan [email protected] School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
gation system. The integrity measure
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