A region-based SLAM algorithm capturing metric, topological, and semantic properties

Abstract

This paper proposes a SLAM algorithm based on FastSLAM 2.0 that maps features representing regions with a semantic type, topological properties, and an approximative geometric extent. The resulting maps enable spatial reasoning on a semantic level and provide abstract information allowing efficient semantic planning and a convenient interface for human-machine interaction. We present novel region features and an algorithm for estimating the feature parameters from uncertain measurements. In particular, we provide a means of estimating parameters even if the region feature is considerably larger than the robot's sensor range. Finally, we adapt the FastSLAM 2.0 algorithm to map the proposed features and show simulation-based results illustrating the capabilities of the proposed algorithm.