移动机器人改进激光SLAM算法研究

doi:10.3778/j.issn.1002-8331.2009-0264

摘要/Abstract

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。

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）

Abstract: 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.

Key words: mobile robot, simultaneous localization and mapping（SLAM）, point cloud registration, particle filtering, resampling, robot operating system（ROS）

陈丹, 吴欣. 移动机器人改进激光SLAM算法研究[J]. 计算机工程与应用, 2022, 58(4): 163-168.

CHEN Dan, WU Xin. Research on Improved Laser SLAM Algorithm for Mobile Robots[J]. Computer Engineering and Applications, 2022, 58(4): 163-168.

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