摘要:
提出一种基于粒子滤波器的机器人定位算法. 首先利用一并行扩展卡尔曼滤波器作为粒子预测分布, 将当前观测的部分信息融入, 以改善滤波效果, 减小所需粒子数; 然后提出变密度函数边界的马尔可夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)重采样方法, 以提高粒子的细化能力; 最后结合普通重采样方法, 提出一种改进的MCMC重采样的机器人定位算法, 减少粒子匮乏效应的同时, 提高了定位精度. 实验结果表明, 该算法较传统方法在计算复杂度、定位精度和鲁棒性方面都有显著提高.
Abstract:
A robot localization algorithm based on particle filter is presented. Firstly, in order to improve the filtering effect and decrease the number of particles needed, one parallel extended Kalman filter is used as the proposal density of particle filter, thus partial observation information can be infused into the filtering process. Secondly, in order to enhance the particles' refining capacity, one improved Markov chain Monte Carlo (MCMC) resampling method with variable boundary of proposal density is put forward. Finally, the robot localization algorithm with the improved MCMC resampling is established, thus the effect of particle impoverishment can be decreased and the localization accuracy can be improved. Experiment results show that this algorithm has the advantages in computational complexity, localization accuracy and robustness.
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