移动机器人改进激光SLAM算法研究  

Research on Improved Laser SLAM Algorithm for Mobile Robots

作　　者：陈丹[1] 吴欣 CHEN Dan;WU Xin(School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China)

机构地区：[1]西安理工大学自动化与信息工程学院,西安710048

出　　处：《计算机工程与应用》2022年第4期163-168,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(61671375);榆林市科技计划(2019-146);西安理工大学研究生培育项目(252051834)。

摘　　要：针对传统RBPF(Rao-Blackwellised particle filter)算法存在定位精度低、粒子退化、粒子多样性丧失的问题,提出了一种基于激光雷达的改进SLAM(simultaneous localization and mapping)算法。首先基于主成分分析法对相邻帧的点云进行粗配准,再采用改进点到线迭代最近点配准算法校正粗配准结果完成精确配准。改进重采样算法中,在多次复制大权重粒子集合的情况下引入小权重粒子集合,改善粒子多样性缺乏问题,提高了移动机器人定位精度。最后将改进算法应用于Turtlebot机器人,实验结果表明,改进的基于激光雷达的SLAM算法在定位精度和建图准确度方面相比于传统算法效果更好。Aiming at the problems of traditional RBPF(Rao-Blackwellised particle filter)algorithm with low positioning accuracy,particle degradation,and loss of particle diversity,an improved SLAM(simultaneous localization and mapping)algorithm based on lidar is proposed.Firstly,the principal component analysis method is used to coarsely register the point clouds between adjacent frames,and then the improved point-to-line iterative nearest point registration algorithm is used to correct the coarse registration results to complete the precise registration.In the improved resampling algorithm,a small-weight particle set is introduced when the large-weight particle set is copied multiple times,which improves the lack of particle diversity and improves the positioning accuracy of the mobile robot.Finally,the improved algorithm is applied to the Turtlebot robot.The test results show that the improved SLAM algorithm based on lidar in this paper is better than the traditional algorithm in terms of positioning accuracy and mapping accuracy.

关 键 词：移动机器人 同步定位与地图构建(SLAM) 点云配准 粒子滤波 重采样 机器人操作系统(ROS) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]