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

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.