无人车蛙跳协同的激光SLAM退化校正  

Degeneration Correction of LiDAR SLAM for UGV Leapfrog Cooperation

作　　者：金浙 蒋朝阳 JIN Zhe;JIANG Chaoyang(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学机械与车辆学院,北京100081

出　　处：《兵工学报》2025年第3期57-65,共9页Acta Armamentarii

基　　金：国家自然科学基金项目(U20A20333)。

摘　　要：稳定高精度的定位是实现地面无人车辆协同自主行驶的先决条件。激光同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术在缺少几何特征的走廊、隧道、沙漠等场景中难以实现精准定位。为此提出一种无人车蛙跳协同的激光SLAM退化校正方法。估计当前帧每个特征点的法向量,并提出一种激光SLAM退化检测算法,当检测到环境退化时,使用两个无人车之间的测距信息对激光SLAM进行退化校正,在位姿图中进一步优化定位结果,并在自主搭建的两个无人车平台上进行测试。研究结果表明,新方法与当前主流激光SLAM方法相比获得了更高的建图效果,证明了新方法能够显著提高激光SLAM在退化场景中的定位效果。Stable and high-precision localization is a prerequisite for realizing the cooperative autonomous navigation of unmanned ground vehicle(UGV).LiDAR simultaneous localization and mapping(SLAM)often fails to achieve the precise localization in scenarios lacking geometric features,such as corridors,tunnels,and deserts.Therefore,a leapfrog cooperative LiDAR SLAM degradation correction method is proposed for UGVs.This method is used to estimate the normal vector of each feature point in the current frame,and a LiDAR SLAM degradation detection algorithm is devised.When the degradation of environment is detected,the ranging information about two unmanned vehicles is utilized to correct the degradation in LiDAR SLAM.Finally,the locating results are further optimized in the pose graph.Testing on two self-built UGV platforms reveals that the proposed method achieves better mapping performance compared to the current famous LiDAR SLAM methods,demonstrating its significant capability to enhance the locating performance of LiDAR SLAM in degraded scenarios.

关 键 词：无人车 蛙跳协同 同时定位与建图 退化检测 退化校正 

分 类 号：TN958.98[电子电信—信号与信息处理]