摘要
由于黑灯工厂中光照条件差且部分区域结构特征相似,为提高移动机器人在黑灯工厂的同步定位与建图算法精度,提出TSR-LIO-SAM算法,将激光雷达与惯性测量单元(IMU)紧耦合,使用预积分因子补偿IMU的动态误差,并利用IMU的高频输出消除激光雷达点云畸变,同时提出基于时空间阈值的关键帧选取与局部建图策略,结合线面特征与点云概率分布来改进迭代最近点(ICP)算法,并配置动态权重优化点云配准。实验结果表明,TSR-LIO-SAM算法相较于LEGO-LOAM和LIO-SAM算法,平均误差分别减少了77.96%、9.77%,均方根误差分别减少了78.64%、8.49%,证明了TSR-LIO-SAM算法在室内环境下的有效性。
The illumination conditions under black factory lights are poor,and the structural characteristics of some areas are similar.Hence,the TSR-LIO-SAM algorithm is proposed to improve the accuracy of the synchronous positioning and mapping algorithm of mobile robots under black factory lights.The LiDAR is tightly coupled with the inertial measurement unit(IMU),and a pre-integration factor is used to compensate the dynamic error of the IMU.The IMU highfrequency output is used to eliminate the LiDAR point cloud distortion.Simultaneously,a key frame selection and local mapping strategy based on the time-space threshold is proposed.The iterative closest point(ICP)algorithm is improved by combining the line-surface features and point cloud probability distribution,and the dynamic weight is configured to optimize the point cloud registration.Experimental results show that,compared with the LEGO-LOAM and LIO-SAM algorithms,the average error of the TSR-LIO-SAM algorithm is reduced by 77.96%and 9.77%respectively,and the root mean square error is reduced by 78.64%and 8.49%respectively,which prove the effectiveness of the TSR-LIOSAM algorithm in indoor environments.
作者
张彬
许晓辉
李海虹
Zhang Bin;Xu Xiaohui;Li Haihong(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,Shanxi,China)
出处
《激光与光电子学进展》
北大核心
2025年第10期145-152,共8页
Laser & Optoelectronics Progress
基金
山西省基础研究计划(202103021224264)
山西省重点研发计划(202302140601012)。
关键词
移动机器人
激光雷达
惯性测量单元
多传感器融合
同时定位与建图算法
mobile robot
LiDAR
inertial measurement unit(IMU)
multi-sensor fusion
simultaneous localization and mapping algorithm
