Submodular Observation Poses Optimization for Mobile Robots

Abstract

In this paper, we address the task where domestic service robots perform object manipulation within a domestic environment following user instructions. Since objects are often moved in daily life, it is important to efficiently perform periodic searches to obtain the latest object positions. The more images collected, the more objects can be located, but this is time-consuming. Focusing on a small number of images can reduce the time required, however, the amount of information obtained is limited by the possibility of obtaining images of walls, corridors with few objects, and so on. Most existing methods do not properly account for the possibility that everyday objects may move to different positions at different points in time. In this paper, we propose an observation pose optimization method that takes advantage of object presence maps and submodularity. The proposed method outperformed the baseline method in terms of the ratio of everyday objects that can be observed in the observations.