Lightweight Loop Closure Detection of Off-road Environment Based on Ground Features

doi:10.12382/bgxb.2024.0090

Abstract

Abstract:

The traditional loop closure detection algorithm relies on the accuracy of odometer and the external global positioning information,which consumes too much computing resources,and the existing lightweight loop closure detection algorithm has poor translation invariance and difficulty in adapting to the sparse environmental characteristics in off-road road environment.In order to improve the positioning capability of unmanned platform in the condition of satellite rejection for a long time and a large range of tasks,a lightweight loop closure detection algorithm using light detection and ranging (LiDAR) point clouds to describe the ground feature is proposed.It is different from extracting the point cloud features from single or multi-frame point clouds by deep learning.And a global descriptor is constructed.The fast LiDAR point clouds ground feature description approach is used to achieve the fast feature extraction of single frame point cloud and the globally consistent position feature description,and the multi-frame LiDAR point clouds ground features are aggregated into the sub-map loop closure detection descriptors.A lightweight global descriptor is constructed by odometer pose between adjacent frames,and the global descriptors are matched and the loop closure detection is realized without prior position information.The proposed algorithm is verified by using the mechanical and solid-state LiDAR in off-road environment.Compared with the existing lightweight loop closure detection algorithms,the proposed algorithm has the advantages of high recall rate,good real-time performance and less resource consumption in the off-road environment.

Key words: off-road environment, light detection and ranging, ground feature, loop closure detection

ZHANG Senjie, GONG Jianwei, QI Jianyong, ZANG Zheng, HU Xiuzhong, GONG Xiaojie, XIONG Guangming. Lightweight Loop Closure Detection of Off-road Environment Based on Ground Features[J]. Acta Armamentarii, 2025, 46(4): 240090-.

Figures/Tables 14

References 23

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通道	内容
B	地面点云回波强度,并从0~255归一化至0~155
G	3.1中计算的角度中大于5°的部分,并从5°~90°归一化至0~255
R	3.1中计算的角度中大于15°的部分,并从15°~90°归一化至0~255

通道	内容
B	地面点云回波强度,并从0~255归一化至0~155
G	3.1中计算的角度中大于5°的部分,并从5°~90°归一化至0~255
R	3.1中计算的角度中大于15°的部分,并从15°~90°归一化至0~255

名称	编号	LiDAR数据来源	环境路况描述
测试Ⅰ	Ⅰ	Velodye_HDL_64	城市环境
测试Ⅱ	Ⅱ	RS_Helios_32	轨迹1
测试Ⅲ	Ⅲ-1	RS_Helios_32	轨迹4
	Ⅲ-2	RS_Helios_32	轨迹4反向
	Ⅲ-3	RS_LiDAR_M1	轨迹4
	Ⅲ-4	RS_LiDAR_M1	轨迹4反向
测试Ⅳ	Ⅳ-1	RS_Helios_32	轨迹2
测试Ⅳ	Ⅳ-2	RS_Helios_32	轨迹3

名称	编号	LiDAR数据来源	环境路况描述
测试Ⅰ	Ⅰ	Velodye_HDL_64	城市环境
测试Ⅱ	Ⅱ	RS_Helios_32	轨迹1
测试Ⅲ	Ⅲ-1	RS_Helios_32	轨迹4
	Ⅲ-2	RS_Helios_32	轨迹4反向
	Ⅲ-3	RS_LiDAR_M1	轨迹4
	Ⅲ-4	RS_LiDAR_M1	轨迹4反向
测试Ⅳ	Ⅳ-1	RS_Helios_32	轨迹2
测试Ⅳ	Ⅳ-2	RS_Helios_32	轨迹3

回环检测模块	LiDAR数据来源
回环检测模块	Velodye_HDL_64	RS_Helios_32	RS_LiDAR_M1
提取地面点	10.44	3.67	3.87
计算法向量	13.47	5.46	7.17
地面点插补至子地图	1.10	0.36	0.35
计算子地图描述子(每5s)	80	52.83	37.05
粗匹配	2.73	2.67	2.23
精匹配	→0	→0	→0
平均每帧总耗时	29.34	13.21	14.36