A Novel Particle Filter Based SLAM Algorithm for Lunar Navigation and Exploration

This paper presents a simultaneous localization and mapping (SLAM) system for lunar exploration. The proposed SLAM algorithm presents a significantly lower computational complexity compared to the state-of-the-art solutions, due the use of a Rao-Blackwellised particle filter with adaptive and compound resampling. The proposed SLAM sensor system consists of one light detecting and ranging sensor (LIDAR) and one IMU, to minimize illumination-dependent errors; as the lunar environment, and in particular the target exploration region around the Marius Hills hole, presents very variable illumination conditions. The system was tested via simulation, using existing environmental data from the mare tranquillitatis pit crater.