摘要： 针对基于RBPF的激光SLAM算法在重采样过程中出现的样本贫化和激光测量模型不准确的问题，提出一种优化的激光SLAM算法。为缓解重采样过程中的样本贫化问题，采用最小采样方差重采样方法改进原重采样方法，使重采样后的粒子保持多样性。结合似然域模型与意外对象观测概率，使激光测量模型更好地反映真实环境。实验结果表明，改进的重采样方法定位效果较好，相对原激光SLAM算法，改进的激光SLAM算法在动态环境中的建图和定位精度更高。

关键词: 激光SLAM算法, 样本贫化问题, 最小采样方差, 激光测量模型, 似然域模型

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.

Key words: laser SLAM algorithm, sample dilution problem, Minimum Sampling Variance(MSV), laser measurement model, likelihood field model

中图分类号: 

• TP242.6