Novel Mobile Robot Simultaneous Loclization and Mapping Using Rao-Blackwellised Particle Filter

Abstract

This paper presents the novel method of mobile robot simultaneous localization and mapping (SLAM), which is implemented by using the Rao-Blackwellised particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation. The landmark position estimation and update is implemented through the unscented transform (UT). Furthermore, the number of resampling steps is determined adaptively, which seriously reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks, which are structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree in the time cost of O(log2 N ). Experiments on the robot Pioneer3 in our real indoor environment show that our method is of high precision and stability.