无人车蛙跳协同的激光SLAM退化校正

doi:10.12382/bgxb.2024.0161

摘要/Abstract

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。

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

Abstract:

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.

Key words: unmanned ground vehicle, leapfrog cooperation, simultaneous localization and mapping, degeneration detection, degeneration correction

中图分类号:

TN958.98

金浙, 蒋朝阳. 无人车蛙跳协同的激光SLAM退化校正[J]. 兵工学报, 2025, 46(3): 240161-.

JIN Zhe, JIANG Chaoyang. Degeneration Correction of LiDAR SLAM for UGV Leapfrog Cooperation[J]. Acta Armamentarii, 2025, 46(3): 240161-.

图/表 11

图1 无人车蛙跳协同定位示意图

Fig.1 Schematic diagram of UGV leapfrog collaborative localization

图2 系统框架

Fig.2 System framework

图3 激光点云特征提取示意图

Fig.3 LiDAR point cloud feature extraction

图4 基于点云法向量分布特性的退化检测

Fig.4 Degradation detection based on the characteristics of normal vector distribution in point cloud

图5 基于测距信息的退化校正

Fig.5 Degradation correction based on ranging information

图6 实验平台和实验场景

Fig.6 Experimental platforms and experimental scenario

图7 退化指数在实验场景中的分布

Fig.7 Distribution of degradation degrees in experimental scenarios

图8 不同方法的定位结果

Fig.8 Localizating results of different methods

表1 不同方法建图结果的点-网格距离统计结果

Table 1 Statistical results of point-mesh distances of mapping results of the different methods

方法	平均距离/m	距离标准差/m
DLO	0.089735	0.122395
A-LOAM	偏差较大
LeGO-LOAM	偏差较大
FAST-LIO2	0.133153	0.142496
LIO-SAM	失效
本文方法	0.083232	0.0927721

图9 不同方法建图结果的点-网格距离分布

Fig.9 Distributions of point-mesh distances of mapping results of the different methods

图10 本文方法的建图结果

Fig.10 Mapping results of the proposed method

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