Qualitative Map Learning Based on Co-visibility of Objects

This paper proposes a unique map learning method for mobile robots based on the co-visibility infor­ mation of objects i.e., the information on whether two objects are visible at the same time or not from the current position. This method first esti­ mates empirical distances among the objects using a simple heuristics - "a pair of objects observed at the same time more frequently is likely to be lo­ cated more closely together". Then it computes all the coordinates of the objects by multidimensional scaling (MDS) technique. In the latter part of this paper, it is shown that the proposed method is able to learn qualitatively very accurate maps though it uses only such primitive information, and that it is robust against some kinds of object recognition er­ rors.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Takehisa Yairi, Koichi Hori
• Keywords: Co-visibility Infor­ Mation, IJCAI 2003, IJCAI 2007, Map Learning Method, Mates Empirical Distances