稳定静态点云簇支持的LiDAR SLAM回环检测方法

doi:10.11947/j.AGCS.2025.20250252

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2194-2205.doi: 10.11947/j.AGCS.2025.20250252

稳定静态点云簇支持的LiDAR SLAM回环检测方法

高佳鑫¹(), 隋心¹^,², 王长强¹, 徐爱功¹(), 史政旭¹

^1.辽宁工程技术大学测绘与地理科学学院，辽宁　阜新　123000
^2.辽宁工程技术大学鄂尔多斯研究院，内蒙古　鄂尔多斯　017000

收稿日期:2025-06-20 修回日期:2025-11-04 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 徐爱功 E-mail:gao_jx9903@163.com;xu_ag@126.com
作者简介:高佳鑫（1999—），男，博士，研究方向为卫星定位与导航。　E-mail：gao_jx9903@163.com
基金资助:
国家自然科学基金(42404045);辽宁省自然科学基金计划博士科研启动项目(2024-BS-256);辽宁省教育厅基本科研项目(LJ212410147093);辽宁工程技术大学鄂尔多斯研究院校地科技合作培育项目(YJY-XD-2024-B-007)

Loop closure detection method for LiDAR SLAM supported by stable static point cloud clusters

Jiaxin GAO¹(), Xin SUI¹^,², Changqiang WANG¹, Aigong XU¹(), Zhengxu SHI¹

^1.School of Geomatics, Liaoning Technical University, Fuxin 123000, China
^2.Ordos Institute, Liaoning Technical University, Ordos 017000, China

Received:2025-06-20 Revised:2025-11-04 Online:2026-01-15 Published:2026-01-15
Contact: Aigong XU E-mail:gao_jx9903@163.com;xu_ag@126.com
About author:GAO Jiaxin (1999—), male, PhD, majors in satellite positioning and navigation. E-mail: gao_jx9903@163.com
Supported by:
The National Natural Science Foundation of China(42404045);The Liaoning Provincial Natural Science Foundation for the Doctoral Research Initiation Project(2024-BS-256);The Basic Research Projects of Liaoning Provincial Department of Education(LJ212410147093);The University-local Government Scientific and Technical Cooperation Cultivation Project of Ordos Institute-LNTU(YJY-XD-2024-B-007)

摘要/Abstract

摘要：

针对动态、退化、大规模杂乱场景中仅基于点云处理的回环检测方法稳健性差，且现有方法普遍存在平移敏感性弱和计算效率低的问题，本文提出了一种基于稳定静态点云簇词袋的回环检测方法。首先，针对预处理后的点云从环境结构角度评价其退化情况，并设计稳健的点云簇筛选方案获取稳定静态点云簇，以削弱动态目标干扰。然后，为减少回环信息冗余，采用模糊综合评价算法适应性地进行关键帧筛选。最后，基于稳定静态点云簇和关键帧筛选结果，提出一种基于点云簇局部描述子词袋的回环检测算法，利用稳定点云簇间相对空间关系以及属性联系提高词袋信息的平移及旋转敏感性，进而保证回环检测在退化及杂乱场景中的实际性能。试验结果表明，在实测场景中，本文方法能够稳健检测正确回环关系，且非回环帧误检率仅为5.56%，处理单帧关键帧耗时为0.052 8 s；相较于BoW3D、ISC、SGLC 3个同类对比方法，回环帧正确检测率平均提高了75.73%，非回环帧误检率平均降低了81.93%，且处理过程具备较强实时性，并展现出更强的稳健性和适用性。

关键词: 点云退化评价, 点云簇分类, 关键帧筛选, 词袋模型, 回环检测

Abstract:

In dynamic, degenerate, and large-scale cluttered environments, loop closure detection methods based solely on point cloud processing exhibit poor robustness. Moreover, existing methods generally suffer from weak translation sensitivity and low computational efficiency. To address these challenges, this paper proposes a bag-of-words with stable static point cloud clusters-based loop closure detection method. Firstly, the degradation of the preprocessed point cloud is evaluated from the environmental structure perspective, and a robust point cloud cluster classification scheme is designed to obtain the stable static point cloud clusters to weaken the interference of dynamic targets. Subsequently, to reduce the redundancy in loop closure information, the fuzzy comprehensive evaluation algorithm is used to adaptively filter the key frames. Finally, based on the stable static point cloud cluster and keyframe selection results, a bag-of-words with point cloud cluster local descriptors-based loop closure detection algorithm is proposed. The relative spatial relationship and attribute relationship between stable point cloud clusters are used to improve the translation and rotation sensitivity of bag information, so as to ensure the actual performance of loop closure detection in degenerate and cluttered scenes. Experimental results demonstrate that the proposed method can robustly detect the correct loop closure relationship in the measured scene, and the non-loop closure frame error detection rate is only 5.56%, with a single-keyframe processing time of 0.052 8 s. Compared with three similar methods BoW3D, ISC, and SGLC, the average improvement in the loop closure frame correct detection rate reaches 75.73%, the average reduction in the non-loop closure frame error detection rate is 81.93%, the processing has strong real-time performance, and it exhibits stronger robustness and applicability.

Key words: point cloud degradation evaluation, point cloud cluster classification, keyframe selection, bag-of-words model, loop closure detection

中图分类号:

P228

高佳鑫, 隋心, 王长强, 徐爱功, 史政旭. 稳定静态点云簇支持的LiDAR SLAM回环检测方法[J]. 测绘学报, 2025, 54(12): 2194-2205.

Jiaxin GAO, Xin SUI, Changqiang WANG, Aigong XU, Zhengxu SHI. Loop closure detection method for LiDAR SLAM supported by stable static point cloud clusters[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2194-2205.

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http://xb.chinasmp.com/CN/Y2025/V54/I12/2194

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方法	场景	ηr	η_e	t
BoW3D	1	1.000 0	0.166 7	0.053 8
	2	0.733 3	1.000 0	0.056 7
	3	1.000 0	0.250 0	0.073 4
	均值	0.911 1	0.472 2	0.061 3
ISC	1	0.000 0	0.000 0	0.071 0
	2	0.266 7	0.500 0	0.072 6
	3	0.692 3	0.000 0	0.096 2
	均值	0.319 7	0.166 7	0.079 9
SGLC	1	1.000 0	0.333 3	0.045 6
	2	0.866 7	1.000 0	0.046 1
	3	1.000 0	0.500 0	0.050 2
	均值	0.955 6	0.611 1	0.047 3
本文方法	1	1.000 0	0.166 7	0.046 7
	2	1.000 0	0.000 0	0.048 9
	3	1.000 0	0.000 0	0.062 8
	均值	1.000 0	0.055 6	0.052 8

算法	RMSE/m	ME/m	单帧耗时/s
LeGO-LOAM	13.456 9	86.224 1	0.037 2
U-LeGO-LOAM	1.249 9	4.528 8	0.047 6

稳定静态点云簇支持的LiDAR SLAM回环检测方法

Loop closure detection method for LiDAR SLAM supported by stable static point cloud clusters

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场景	去程关键帧	返程关键帧	返程回环帧	返程非回环帧
1	12	15	9	6
2	17	17	15	2
3	15	17	13	4