基于全局特征描述子的激光SLAM回环检测方法

doi:10.16183/j.cnki.jsjtu.2021.202

摘要/Abstract

摘要：

针对井下环境中设备巡检时定位误差随时间不断累积的问题,提出一种适用于激光同步定位和建图技术的基于点云全局特征描述子的回环检测方法.该方法利用曲率计算点云中特征点的特征向量,通过特征向量与中心点坐标系的角度分布和尺度分布关系构建点云全局特征描述子;使用部分特征点进行位姿变换的计算,提高计算效率.通过仿真实验和开源数据集实验对所提算法进行验证.实验结果表明,所提算法在定位精度和实时性上提升明显,可以有效解决定位算法在长时间运行过程中的累积误差和全局一致性差的问题.

关键词: 井下巡检, 激光定位, 回环检测, 全局特征描述子

Abstract:

To solve the problem that localization error of the underground inspection system continues to accumulate over time, a loop closure detection algorithm based on point cloud global feature descriptor is proposed, which is suitable for laser simultaneous localization and mapping (SLAM). The feature vector of each point in point cloud is calculated by curvature, then the global feature descriptor of point cloud is constructed based on the angle distribution and scale distribution relationship between the feature vector and center point coordinate system. In addition, the pose transformation of two similar frames is calculated by feature point registration to improve computing efficiency. The proposed algorithm is verified by simulation experiments and open-source data experiments. The experimental results show that the proposed algorithm has a significant improvement in localization accuracy and real-time performance, which can effectively solve the problems of increased cumulative error and poor global consistency of the localization algorithm during long-term inspections.

Key words: underground inspection, laser localization, loop closure detection, global feature descriptor

中图分类号:

TP242

韩超, 陈敏, 黄宇昊, 赵明辉, 杜乾坤, 梁庆华. 基于全局特征描述子的激光SLAM回环检测方法[J]. 上海交通大学学报, 2022, 56(10): 1379-1387.

HAN Chao, CHEN Min, HUANG Yuhao, ZHAO Minghui, DU Qiankun, LIANG Qinhua. Loop Closure Detection Method of Laser SLAM Based on Global Feature Descriptor[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1379-1387.

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序号	实验算法	最大误差/m	终点误差/m	均方根误差/m	巡检距离/m
1	LOAM	3.48863	3.45962	1.52933	618.007
	LOAM+ICP	2.50563	0.14402	1.03654	618.007
	本文算法	1.46447	0.06376	0.58827	618.007
2	LOAM	1.69615	1.53225	0.83298	638.446
	LOAM+ICP	0.54603	0.02760	0.32963	638.446
	本文算法	0.40609	0.02806	0.17783	638.446
3	LOAM	1.16494	1.15254	0.71497	264.852
	LOAM+ICP	0.82921	0.08813	0.43781	264.852
	本文算法	0.47062	0.09923	0.21525	264.852
4	LOAM	1.92765	0.16456	1.06969	197.782
	LOAM+ICP	0.45537	0.04628	0.22482	197.782
	本文算法	0.39201	0.05806	0.15977	197.782

算法	相似度检测时间/ms	位姿计算时间/ms	回环时间/ms
LOAM+ICP	12.6676	122.3668	147.0491
本文算法	16.2595	67.4053	88.7274