Viewpoint based mobile robotic exploration aiding object search in indoor environment

Abstract

We present a probabilistic method of finding the next best viewpoint that maximizes the chances of finding an object in a known environment for an indoor mobile robot. We make use of the information that is available to a robot in the form of potential locations to search for an object. Extraction of these potential locations and their representation for exploration is explained. This work primarily focuses on placing the robot at its best location in the environment to detect, recognize an object and hence do object search. With experiments done on the exploration, object recognition individually we show the robustness of this approach for object search task. We analyse and compare our method with two other strategies for localizing the object empirically and show unequivocally that the strategy based on the probabilistic formalism in general performs better than the other two.