Subjective local maps for hybrid metric-topological SLAM

Abstract

Hybrid maps where local metric submaps are kept in the nodes of a graph-based topological structure are gaining relevance as the focus of robot Simultaneous Localization and Mapping (SLAM) shifts towards spatial scalability and long-term operation. In this paper we examine the applicability of spectral graph partitioning techniques to the automatic generation of metric submaps by establishing groups in the sequence of observations gathered by the robot. One of the main aims of this work is to provide a probabilistically grounded interpretation of such a partitioning technique in the context of generating local maps. We also discuss how to apply it to different kinds of sensory data (landmarks extracted from stereo images and laser range scans) and how to consider them simultaneously. An important feature of our approach is that the partitioning takes into account the intrinsic characteristics of the sensors, such as the sensor field of view, instead of applying heuristics supplied by a human as in other works. Thus the robot builds “subjective” local maps whose size will be determined by the nature of the sensors. The ideas presented here are supported by experimental results from a real mobile robot as well as simulations for statistical analysis. We discuss the effects of considering different combinations of sensors in the resulting clustering of the environment.