Visual odometry errors and fault distinction for integrity monitoring

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.