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复杂地形环境下的多传感器融合SLAM技术OA北大核心CSTPCD

Multi-sensor Fusion SLAM in Complex Terrain Environments

中文摘要英文摘要

针对在野外、森林、山地或建筑工地等复杂环境中,SLAM(同时定位与地图构建)算法精度下降、定位漂移或甚至失效的问题,提出一种复杂地形环境下的多传感器融合SLAM算法.首先,针对剧烈运动时点云畸变严重的问题,提出了一种自适应划分雷达帧的子帧分割方法,并利用IMU(惯性测量单元)预积分技术进行点云畸变补偿,降低点云帧内失真,提高SLAM算法在剧烈运动时的鲁棒性;然后,算法前端基于迭代误差状态卡尔曼滤波(IESKF)算法,融合激光雷达、IMU的数据进行状态估计,为后端提供精确的初始位姿;后端基于因子图,融合前端激光惯导里程计因子、闭环检测因子、全球定位系统(GPS)因子,提高SLAM算法的准确性和全局一致性.最后,在剧烈运动场景、校园综合场景以及野外森林场景中对算法进行了测试.实验结果显示,与FAST-LIO2算法和LIO-SAM算法相比,本文算法的定位精度更高,建图更加清晰,在剧烈运动场景中更具鲁棒性.

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.

路春晓;钟焕;刘威;周勇;崔智全;李卫华

哈尔滨工业大学(威海)汽车工程学院,山东威海 264209中建三局集团有限公司,湖北武汉 430070哈尔滨工业大学(威海)汽车工程学院,山东威海 264209||长三角哈特机器人产业技术研究院,安徽芜湖 241060

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

simultaneous localization and mappingiterative error-state Kalman filterfactor graphsensor fusion

《机器人》 2024 (004)

425-435 / 11

国家自然科学基金(52175007,52175012);中建三局集团有限公司产学研平台课题(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).

10.13973/j.cnki.robot.230288

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