摘要
基于改进粒子滤波器,提出了一种应用于未知环境下的移动机器人的同步定位与地图创建方法.针对传统粒子滤波器经过多次迭代后粒子退化从而需要大量粒子才能提高定位精度的问题,设计了一种基于人工鱼群算法的粒子滤波算法,该方法主要利用人工鱼群算法对预估粒子进行二次更新,从而调整了粒子的分布使其更加接近真实位姿,提高机器人的SLAM性能.经过Matlab仿真实验,证明了该方法能够准确快速地对机器人定位,并且构建的地图精度也很高.
A mobile robot SLAM （simultaneous localization and mapping） method in unknown environment based on improved particle filter was proposed. The particles degradation of the traditional particle filter and the need of a large number for particles to improve the precision of robot location were focused. AFSA （artificial fishing-swarm algorithm） was introduced into the particle filter method. This method updates the particles＇ prediction by using she AFSA which could adjust the distribution of particles to concentrate upon the robotts true pose. As a result, the ability of SLAM is enhanced. Through the Matlab simulation, results show that the method can locate the robot quickly and accurately, and improve the mapping precision.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第7期9-13,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家高技术研究发展计划资助项目(2007AA041501)
关键词
移动机器人
粒子滤波器
同步定位与地图创建
粒子退化
人工鱼群算法
mobile robot
particle tilter
simultaneous locahzatlon and mapping （SLAM）
particlesdegradation
artificial fishing-swarm algorithm
