基于改进Cartographer算法的激光SLAM研究
Research on Laser SLAM Based on Improved Cartographer Algorithm
苗红霞 1郭章旺 2齐本胜 2相志敏2
作者信息
- 1. 河海大学物联网工程学院,江苏 常州 213000;江苏省输配电装备技术重点实验室,江苏 常州 213000
- 2. 河海大学物联网工程学院,江苏 常州 213000
- 折叠
摘要
针对激光SLAM中Cartographer算法在IMU数据不可靠时,位姿预测误差较大、扫描匹配时间较长的问题,提出了一种改进的Cartographer算法.首先,在预测先验位姿时,使用位姿数据队列末尾的四个数据来计算移动机器人的线速度,并采用加权融合的方法,对移动机器人的旋转增量进行修正;其次,在使用实时相关性扫描匹配校准先验位姿时,先采用稀疏化策略获取候选位姿旋转得分的分布情况,再对候选位姿得分小于阈值的区域进行剪枝处理,最后对保留下的候选位姿区域计算平移加权得分.仿真表明,在IMU数据不可靠时,改进的Cartographer算法能够有效地改善位姿预测效果,并在保证激光SLAM建图质量的基础上,缩短了实时相关性扫描匹配的时间.
Abstract
In order to solve the problem of large pose prediction error and long scan matching time of the Cartog-rapher algorithm in laser SLAM when IMU data is not reliable,an improved Cartographer algorithm is proposed.First,when predicting the prior pose,the linear velocity of the mobile robot is calculated by using the four data at the end of the data queue,and the rotation increment of the mobile robot is corrected by using the weighted fusion method;Sec-ondly,when using real-time correlation scanning matching to calibrate a priori pose,first use the sparse strategy to ob-tain the distribution of candidate pose rotation scores,then prune the areas whose candidate pose scores are less than the threshold,and finally calculate the translation weighted scores for the reserved candidate pose areas.The simula-tion shows that the improved Cartographer algorithm can effectively improve the position and attitude prediction effect when IMU data is not reliable,and shorten the time of real-time correlation scanning and matching on the basis of en-suring the quality of laser SLAM mapping.
关键词
激光同步定位与建图/位姿预测/加权融合/扫描匹配Key words
Lasers simultaneous localization and mapping/Pose prediction/Weighted fusion/Scan match引用本文复制引用
基金项目
江苏省输配电装备技术重点实验室开放课题(2021JSSPD05)
常州市应用基础研究课题(CJ20220083)
河海大学大学生创新创业课题(2021102941422)
出版年
2024
NETL
NSTL
万方数据