Localization And Map Building Based On Particle Filter In Large Environment For An AUV

SLAM(simultaneous localization and mapping)problem has been a hot issue in the field of robots. It means building an incremental map while also using it for localization, which is of prime importance for mobile robot navigation. With the development of research and the no-structured and more complex of the environments that mobile robots are used, SLAM has been the foundation work of the robot research. SLAM is considered as the core issue of autonomous robots because of its important theoretical and application value. SLAM is the key problem of AUV navigation.The thesis first reviews the development and application of SLAM, which introduces the development and evolvement of SLAM and its in-depth application in robot in detail. Based on the basic principle on localization and mapping, the emphases of this paper are introduced: first are the EKF-based SLAM and PF-based SLAM, and then introduces the principle and reasoning process of RBPF-SLAM, the four steps of RBPF is introduced as well. This algorithm researches deeply on the advantages of the RBPF-SLAM. Aiming at lowering the errors introduced by linearity and less features, the thesis adopts UKF to incorporate the the current observations as well as the historical observations into the proposal distribution, which is a great improvement. When used to AUV, the matlab simulation results show that the improved algorithm has higher location accuracy. The experimental data shows that the accuracy and convergence of location are increased greatly with this improved algorithm to deal with robot location in large environment. The principle of this thesis is to apply improved RBPF to SLAM. This approach factors the full SLAM posterior exactly into a product of a robot path posterior and landmark posteriors conditioned on the robot path estimate. Compared to traditional method, the new method higher accuracy.This paper is base on the 863 item"simultaneous localization and mapping for AUV navigation", so it has great practical value.