摘要：This study presents the novel RBPF for mobile robot SLAM using stereovision to extract landmark information. The particle filter is combined with Gaussian Mixture Unscented Particle Filters (GMUPF) to extending the path posterior by sampling new poses that integrate the current observation that drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through GMUPF which a single update step from moving and sensing can be done and the change to the map certainty can be done in constant time. Furthermore, the number of resampling steps is determined adaptively, which seriously reduces the particle depletion problem. The 3D natural point landmarks are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree which introduce the Mahalanobis distance instead of the Euclidean distance for matching features in the time cost of O (log2N). Experiment results on real robot in our indoor environment show the advantages of our methods over previous approaches.
