Performance of Temporal Filters for Optical Flow Estimation in Mobile Robot Corridor Centring and Visual Odometry

We present a comparison of three temporal filters used in the estimation of optical flow for mobile robot navigation. Previous comparisons of optical flow and associated techniques have compared performance in terms of accuracy and/or efficiency, and typically in isolation. These comparisons are inadequate for addressing applicability to continuous, real-time operation as part of a robot control loop. We emphasise the need for comparisons that consider the context of a system, and that are confirmed by in-system results. To this end, we give results for on and off-board trials of two biologically inspired behaviours: corridor centring and visual odometry. Our results show the best performing filter for use in the control loop is a recursive temporal filter, out performing the traditionally used Gaussian filter. Results for a large Gaussian filter indicate that long latencies may significantly impede performance for real-time tasks in the control loop.