Improved Positioning and Mapping Method and Optimization of Rao-Blackwellized Particle Filter
Aiming at the problem of autonomous positioning and mapping of unmanned systems in complex ground scenes,from the perspective of robot mapping,a scheme for robots to realize self-positioning in complex environments is proposed.The scheme adopts the cross-mutation idea in genetic algorithm,which improves Rao.The re-sampling process in the synchronization posi-tioning and map construction(RBPF-SLAM)algorithm of the Blackwellized particle filter is optimized.After simulation and self-designed crawler unmanned system experimental platform for experimental verification,the SLAM algorithm optimized by this method enables the grid map constructed by the unmanned system to be drawn with higher accuracy while consuming less par-ticles.The grid map achieves a better effect of autonomous positioning and mapping.
万方数据
维普
