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Research on Rao-Blackwillised Particle Filter SLAM algorithm based on QPSO

In order to solve some problems of the traditional Rao-Blackwillised particle filter (RBPF), which the low precision of the proposed distribution and the particle degeneracy and loss of diversity in the resampling process, a Rao-Blackwillised Particle Filter SLAM algorithm based on Quantum-behaved Particle Swarm Optimization (QPSO) is proposed. The fusion of robot motion model and observation model is proposed as a hybrid proposal distribution to improve the accuracy of the proposed distribution; The introduction of QPSO algorithm update the pose of particles in the process of resampling, according to the weight measurement of particle type adaptive crossover and mutation operation, optimizing and adjusting the particle set, effectively prevent the particle degradation and maintain the diversity of particles. This algorithm not only carry out in the Matlab simulation, and the use of Voyageer-II mobile robot in robot operating system (ROS) to realize the actual verification. The results show that the proposed algorithm can accurately estimate the position and pose of the robot and a high precision map, and error and running time are also greatly reduced.