Efficient Lunar Rover SLAM: Technique and Experiment

X. Li and H. Ju (PRC)


Lunar rover, Localization, Mapping, SLAM, Particle Filter


This paper presents an efficient Simultaneous Localization and Map-Building (SLAM) technique for Lunar Rover navigation based on laser scan-matching and Rao-Blackwellized Particle Filter (RBPF). It is an improved version of FastSLAM algorithm which deals with unstructured environment. As inconsistency remains unsolved in particle filter, a velocity estimation method is adopted and provides robustly converged filter results as no error accumulates in velocity estimations. To reduce computational cost, a fast laser scanning and matching implementation is proposed to facilitate on line processing in real time. Furthermore, all particles share a common map, which allows less memory and computation requirements. Advantages of our proposal are validated by both simulations and real experimental results.

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