In this paper, we develop a new framework for simultaneous localization and mapping (SLAM) based on Rao-Blackwellized particle filters (RBPF), that can be applied to floor-cleaning robots which are equipped with sparse and short-range sensors. To overcome the sensor limitations, the entire region is divided into several local maps, which are assumed to be independent to each other. The local maps are estimated by a local RBPF SLAM, and then the trajectory of the local map origin is estimated by a global RBPF SLAM. To compensate for the severe sensing limitations, we also adopt the assumption that the indoor environments consist of many orthogonal lines. This assumption significantly enhances the filter performance. The proposed SLAM framework is combined with a coverage path planning algorithm, and the resulting robot system is capable of online simultaneous coverage and SLAM. The algorithm was embedded into a real mobile robot platform and tested in a real home environment to assess the robustness of the proposed method.
['Tae-Kyeong Lee', 'Seongsoo Lee', 'Se-Young Oh']
A hierarchical RBPF SLAM for mobile robot coverage in indoor environments