Abstract

Covisibility-based mapping is a paradigm for robotic map building research in which a mobile robot estimates multiple object positions only from ``covisibility'' information, i.e., ``which objects were recognized at a time''. In previous studies on this problem, a solution based on a combination of heuristics - ``closely located objects are likely to be seen simultaneously more often than distant objects'' and Multi-Dimensional Scaling (MDS) was proposed, and it was shown that qualitative spatial relationships among objects are learned with high accuracy by this method. However, theoretical validity of the heuristics has not been sufficiently discussed in these studies. Besides, the existing method has a defect that the quantitative accuracy of built maps is very low. In this paper, we first prove that the heuristics is generally valid in a certain condition, and then present several enhancements to the original method in order to improve the quantitative accuracy of the maps. In the experiments, it was found these enhacements are quite effective.