基于粒子滤波的机器人定位及动态目标跟踪

提出了一种基于粒子滤波的动态跟踪算法,解决了传统SLAM理论在处理动态目标时误差不断累加的问题。通过分析移动机器人和激光测距仪,里程计的原理,建立了机器人的运动和观测模型。将数据关联的方法用于动态环境中则提高了系统的稳定性和定位的精度。仿真结果表明此算法能够比较精确地估计出机器人的位姿以及动态目标在地图中的位置,为开展将静态与动态相结合的定位与地图构建的研究提供了一种可行方案。

Localization and Tracking of Moving Objects Based on Particle Filter for Mobile Robot

An algorithm of tracking of moving objects based on particle filter was proposed.A solution for existing methods of Simultaneous Localization and Mapping (SLAM) was provided to solve the problem of error accumulation while dealing with dynamic targets.Through analyzing the mechanism of mobile robot,laser range finder and odometer sensor,kinematic model and observation model were established.And,system could obtain better robusticity as well as improved accuracy of localization by employing data association in dynamic environment.Simulation results show that the proposed algorithm is able to estimate the pose of mobile robot and the position of moving objects in the map with good precision,and provides a feasible method for further research about SLAM with combination of dynamic localization and static localization.