基于粒子滤波的室内机器人定位研究

Study on Indoor Robot Location Based on Particle Filter

针对传统粒子滤波定位算法在粒子的更新中仅考虑当前里程信息,论文基于KLD-粒子滤波算法实时跟踪每次迭代所需粒子数,提出了一个KLD粒子滤波定位改进算法。结合里程信息及雷达激光测量信息,将前时的测量信息融合进粒子定位算法中。该算法在粒子更新的同时优化了计算,可以使机器人定位修复快速收敛至正确位置,并通过仿真实验验证其有效性。仿真表明,该算法比普通KLD采样有更好的收敛效果,该本仿真条件下,收敛速度比传统方法快约50%以上。

In view of the fact that the traditional particle filter localization algorithm only considers the current mileage infor⁃mation in particle updating,this paper proposes an improved KLD particle filter localization algorithm based on the KLD-particle filter algorithm to track the number of particles required for each iteration in real time.Combining the mileage information and part of the measurement information,the measurement information of lidar is fused into the particle location algorithm.The algorithm op⁃timizes the calculation while updating the particles,which can make the robot localization converge to the correct position quickly,and its validity is verified by simulation experiments.The simulation results show that the algorithm has better convergence effect than ordinary KLD sampling.Under the simulation conditions,the convergence speed is about 50%faster than the traditional meth⁃od.