Abstract: RBPF-based laser SLAM algorithms suffer from sample dilution and inaccurate laser measurement models in the resampling process.To address the problem,this paper proposes an optimized laser SLAM algorithm.In order to alleviate the sample dilution in resampling,Minimum Sampling Variance(MSV) resampling method is used to improve the original resampling method to keep the diversity of the resampled particles.Then the likelihood field model and the probability of unexpected objects are combined to make the laser measurement model better reflect the real environment.Simulation results show that the improved resampling method has excellent performance in positioning,and outperforms the original laser SLAM algorithms in terms of the accuracy of mapping and positioning in dynamic environment.
laser SLAM algorithm,
sample dilution problem,
Minimum Sampling Variance(MSV),
laser measurement model,
likelihood field model