Speed independent terrain classification using Singular Value Decomposition Interpolation

Abstract

Terrain classification is key to using terrain dependent control modes to improve performance of autonomous ground vehicles (AGVs). One of the most viable forms of terrain classification, reaction-based terrain classification, is subject to the problem of speed and load dependency, which requires collecting large data sets for algorithm training. The research presented here presents a method of interpolating point clouds called Singular Value Decomposition Interpolation or SVDI, which uses singular value decomposition, matrix logarithms and Catmull-Rom splines. The estimated point clouds can then substitute for empirical training data, thereby reducing the need to collect large data sets for algorithm training. Here, SVDI is applied to the problem of speed dependency using a mobile robot. Although it is seen that interpolated point clouds are not as effective as real data, interpolated point clouds are seen to be more effective than known point clouds that do not correspond to the desired vehicle speed. Therefore it is concluded that SVDI can effectively reduce the speed and load dependence of reaction-based terrain classification.