Degeneration Correction of LiDAR SLAM for UGV Leapfrog Cooperation

doi:10.12382/bgxb.2024.0161

Abstract

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

CLC Number:

TN958.98

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

Figures/Tables 11

Fig.1 Schematic diagram of UGV leapfrog collaborative localization

Fig.2 System framework

Fig.3 LiDAR point cloud feature extraction

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

Fig.5 Degradation correction based on ranging information

Fig.6 Experimental platforms and experimental scenario

Fig.7 Distribution of degradation degrees in experimental scenarios

Fig.8 Localizating results of different methods

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

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

Fig.10 Mapping results of the proposed method

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