VISION-BASED VELOCITY ESTIMATION FOR UNMANNED GROUND VEHICLES

Abstract

A novel vision-based velocity estimation technique is presented in this paper. A monocular camera rigidly attached to an unmanned ground vehicle (UGV) is used to capture image sequences of the terrain surface and compute the image velocities using an optical flow method. Combining with the proposed camera model, the velocity of the UGV can be directly estimated. This velocity estimation method is validated over coarse sand, fine sand and mixture of coarse sand and gravel separately. Estimated velocities are compared to measured velocities from highly accurate optical encoders, showing the maximum error is less than 1.5%. The effect of feature window size and the distance between the camera projection center and the terrain surface on the velocity estimation is investigated. Random white noise is added to test the robustness of the algorithm and the results are encouraging. The proposed velocity estimation method has many promising potential applications.