Visual localization using an optimal sampling of bags-of-features with entropy applied to repeatable test methods

Abstract

Rough terrain, such as the rubble that one would expect to find in urban disaster areas, poses a severe impediment to robot mobility. The goal of this paper1 is to develop methods for quantifying the difficulty a robot would encounter traversing such a region of rough terrain. Towards this, three traversability metrics describing rough terrain robot mobility are developed. In order to simplify the problem, we assume that the rough terrain in question can be discretized in a certain manner and then develop the metrics for this discretized version of the terrain. Two of the developed metrics reflect the difficulty a robot would have trying to move over the entire region of terrain (referred to as coverability). The third metric, referred to as crossability, describes the difficulty a robot would encounter attempting to move from some fixed point on the terrain to some other fixed point. We verify the applicability of the developed metrics using NIST-developed repeatable test methods (step fields) and discuss the validity of the obtained results.