Research on the improvement of Rao-Blackwellized particle filter for the incremental environment mapping and self-localization of a mobile robot

Aimed at the problem of the incremental environment mapping and self-localization of a mobile robot, the Rao-Blackwellized particle filter (RBPF) algorithm is improved to get the unite estimation of the pose of mobile robot and the position of the environmental landmarks. There are two parts in the RBPF algorithm to be studied. One is that the pose estimation of mobile robot is mended by adapting the resampling process grounded on the effective sample size (ESS) and by adopting mixture Gaussian distribution to approximate proposal distribution so as to improve the sample weight computation in obtaining ESS. The other is that the unscented Kalman filter with the adaptation estimation for the process noise is introduced into the position evaluation of the environmental landmarks. With mobile robot MORCS-1 as experimental platform, the validity of the proposed algorithm in this paper is proved.