Monocular Outlier Detection for Visual Odometry

摘要

In this paper, we propose an optimization scheme to detect outliers in a visual odometry pipeline that is purely based on the optical flow of one monocular camera without requiring information about depth. First, we review different optical flow error measures and uncover the different sensitivity changes of the endpoint error for optical flow to the directional and the absolute error components. Then, we analyse the dependency of the direction of the flow induced by the translational components of the camera motion assuming the flow induced by the rotational camera motion components to be known. Based on a reliable estimate of the focus of expansion, we solve for a normalized directional flow error that takes the influence of the discretization error induced by the pixel size into account. Here, a circular and a squared error bound on the discretization error is investigated. Finally, we present a monocular outlier detection pipeline including the normalized directional flow error as a suitable constant threshold criterion almost invariant to the camera motion and the scene geometry. Further on, evaluations of the overall monocular outlier detection method called FERO included in a stereo visual odometry pipeline are given to compare the performance of monocular against stereoscopic outlier detection based on Kitti benchmark.