机器人2024，Vol.46Issue(4)：425-435,11.DOI:10.13973/j.cnki.robot.230288
复杂地形环境下的多传感器融合SLAM技术
Multi-sensor Fusion SLAM in Complex Terrain Environments
摘要
Abstract
For the problems of precision degradation,localization drift,and even failure of simultaneous localization and mapping(SLAM)algorithms in complex environments such as field,forest,mountain or construction sites,a multi-sensor fusion SLAM algorithm for complex terrains is proposed.Firstly,an adaptive sub-frame segmentation method for radar frames is introduced to address severe point cloud distortion caused by intense motion.This method utilizes IMU(inertial measurement unit)pre-integration to compensate for point cloud distortion,reducing intra-frame distortion and improving the robustness of the SLAM algorithm during intense motion.Secondly,the iterative error-state Kalman filter(IESKF)is employed in the front-end of the algorithm to fuse LiDAR and IMU data for state estimation,providing accurate initial poses for the back-end.In the back-end,the front-end LiDAR-inertial odometry factors,loop closure detection factors,and global positioning system(GPS)factors are integrated based on a factor graph to improve the accuracy and global consistency of the SLAM algorithm.Finally,the proposed method is tested in intense motion scenes,comprehensive campus scenes,and outdoor forest scenes.Experimental results demonstrate that compared to the FAST-LIO2 and LIO-SAM algorithms,the proposed method achieves higher localization accuracy,clearer mapping,and greater robustness in intense motion scenes.关键词
同时定位与地图构建/迭代误差状态卡尔曼滤波(IESKF)/因子图/传感器融合Key words
simultaneous localization and mapping/iterative error-state Kalman filter/factor graph/sensor fusion引用本文复制引用
路春晓,钟焕,刘威,周勇,崔智全,李卫华..复杂地形环境下的多传感器融合SLAM技术[J].机器人,2024,46(4):425-435,11.基金项目
国家自然科学基金(52175007,52175012) （52175007,52175012）
中建三局集团有限公司产学研平台课题(CSCEC-2022-Z-6) （CSCEC-2022-Z-6）
长三角哈特机器人产业技术研究院项目(HIT-CXY-CMP2-ADTIL-21-01). （HIT-CXY-CMP2-ADTIL-21-01）
