A hierarchical SLAM for uncertain range data

This paper describes a new approach to SLAM problems using low quality range data. Vision sensors are useful for acquiring various kinds of environmental information but range data obtained by stereo vision is less reliable than other active sensors like laser range finders. False stereo matches often result in spurious obstacles, which may degrade the map when directly used in existing SLAM methods. We therefore propose a hierarchical approach in which local probabilistic occupancy maps are first generated to filter out such spurious obstacles and then used as inputs to an RBPF-based SLAM. Experimental results in simulation and in a real environment show that a consistent map can be generated by the proposed method with low quality stereo range data.