How to distinguish inliers from outliers in visual odometry for high-speed automotive applications - Buczko, Martin; Willert, Volker (2016 IEEE Intelligent Vehicles Symposium (IV), 2016)
In this paper, we present an outlier removal scheme for stereo-based visual odometry which is especially suited for improving high-speed pose change estimations in large-scale depth environments. First we investigate the variance of the reprojection error on the 3D position of a feature given a fixed error in pose change to conclude that a detection of outliers based on a fixed threshold on the reprojection error is inappropriate. Then we propose an optical flow dependent feature-adaptive scaling of the reprojection error to reach almost invariance to the 3D position of each feature. This feature-adaptive scaling is derived from an approximation showing the relation between longitudinal pose change of the camera, absolute value of the optical flow, and distance of the feature. Using this scaling, we develop an iterative alternating scheme to guide the separation of inliers from outliers. It optimizes the tradeoff between finding a good criterion to remove outliers based on a given pose change and improving the pose change hypothesis based on the current set of inliers. Including the new outlier removal scheme into a pure two-frame stereo-based visual odometry pipeline without applying bundle adjustment or SLAM-filtering we are currently ranked amongst the top camera-based algorithms and furthermore outperform camera and laser scanner methods in Kitti benchmark's high-speed scenarios.