摘要
提出一种基于改进粒子滤波器的移动机器人同时定位与建图方法.该方法将常规粒子滤波器与粒子群优化算法有机结合,引入最新的机器人观测信息以调整粒子的提议分布,从而在保证算法精度的同时,减少定位与建图所需的粒子数,并有效缓解粒子退化现象.此外,考虑到常规的重采样过程容易引起样本贫化现象,引入概率算子以增加粒子的多样性.实验结果表明该方法的可行性和有效性.
An approach to mobile robot simultaneous localization and mapping （SLAM） based on an improved particle filter is presented. The standard particle filter and particle swarm optimization algorithm are incorporated into the filtering framework of the proposed approach. The newest observations are introduced to adjust proposal distribution of particles, and thus the necessary number of particles for localization and mapping is greatly reduced and the particle degeneracy problem is effectively abated. In addition, considering that the typical resampling process always leads to the loss of diversity in particles, a probabilistic operator is introduced to keep the diversity of particle swarm. Experimental results show the feasibility and effectiveness of the proposed approach.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第6期843-848,共6页
Pattern Recognition and Artificial Intelligence
