基于自适应多提议分布粒子滤波的蒙特卡洛定位算法

Monte Carlo localization algorithm based on particle filter with adaptive multi-proposal distribution

针对基于Cubature粒子滤波的蒙特卡罗定位(CMCL)算法存在的计算量大、实时处理能力较差的问题,提出一种基于自适应多提议分布粒子滤波的蒙特卡罗定位(AMPD-MCL)算法。该算法利用Cubature卡尔曼滤波和扩展卡尔曼滤波改进提议分布,融入当前观测信息,减弱粒子退化现象;重采样部分采用Kullback-Leibler距离(KLD)采样,根据粒子在状态空间的分布状况,在线调整下一次滤波迭代所需粒子数,从而减小计算量。仿真实验验证了自适应多提议分布粒子滤波(AMPD-PF)的有效性;同时在机器人操作系统(ROS)上进行实验,结果表明改进算法的平均定位精度达到19.891 cm,定位所需粒子数稳定在60,定位时间为45.543 s,较CMCL算法在定位精度上提高了71.03%,时间缩短了63.10%。实验结果表明,AMPD-MCL算法减小了定位误差,能实时在线调整粒子数,有效减少了算法计算量,提高了实时处理能力。

Concerning the problems of high computation complexity and poor real-time processing capability in Monte Carlo Localization based on Cubature particle filter （ CMCL）, a new Monte Carlo localization algorithm based on particle filter with Adaptive Multi-Proposal Distribution （AMPD-MCL） was proposed. The proposal distribution in this algorithm was improved by using Cubature Kalman filter and the extended Kalman filter, in which the most recent measurements were added to weaken particle set degeneracy phenomenon. According to the distribution of particles in state space, Kullback-Leibler Distance （KLD） sampling was utilized in re-sampling to adjust the number of particles required for the next iteration of the filter, which reduced the amount of computation. Simulation results proved the effectiveness of Particle Filter with Adaptive Multi-Proposal Distribution （ AMPD-PF）. Experiments carried out on the Robot Operating System （ROS） showed that the improved algorithm achieved the average localization accuracy at 19. 891 era, the number of particles needed for localization was 60, and the localization time was 45. 543 s; compared with CMCL algorithm, the localization accuracy was increased by 71.03%, the localization time was shortened by 63. 10%. The resuhs demonstrate that AMPD-MCL algorithm reduces localization error, adjusts the number of particles in real-time, reduces computation cost, and enhances real-time processing capability.