基于自适应方法的移动机器人同时定位与地图构建研究

Research on Simultaneous Localization and Map Construction of Mobile Robot Based on Adaptive Method

传统Rao-Black wellized粒子滤波器用于移动机器人同步定位与地图构建相关问题研究时,具有算法过于复杂、占用内存空间较多、实时性不佳等问题,在此基础上,提出了一种针对性的改进算法。在某个特点状态下一组粒子集内,粒子统计特性具有一致性的特征。而改进算法从中挑选某个代表粒子,逐步更新卡尔曼步骤,并与同一个粒子内集中重复应用。笔者选择某学校研发中心一楼作为实验环境,应用Pioneer III-DX移动机器人展开实验。实验结果证实,这种方法不仅能有效保护栅格地图精度,也可以提升系统的实时性和运算速度,进一步降低操作复杂度。

The traditional Rao-Black particle filter is used in the research of related problems mobile robot synchronization positioning and map building.The algorithm is too complex,occupies more memory space,real-time property is not good.This ppaper proposes an improved algorithm.In a set of particles the statistical properties of particles are consistent.The improved algorithm selects a representative particle,updates the Kalman filter step by step,and repeats the application with the same particle.The author chose the first floor of a school research and development center as the experimental environment,and applied Pioneer III-DX mobile robot to carry out the experiment.The experimental results show that this method not only can effectively protect the grid map accuracy,but also can improve the operation speed of the system,and further reduce the operation complexity.