Loop Closure Detection in 3D Laser-Based SLAM Using Spherical Harmonics Descriptors

被引：0

作者：

Li Z. ^{[1
]}

Wei M. ^{[1
]}

Wu Q. ^{[2
]}

Guo Y. ^{[3
]}

Xu K. ^{[4
]}

Li J. ^{[5
]}

Wang J. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] School of Artificial Intelligence, Anhui University, Hefei

[3] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing

[4] College of Computer Science and Technology, National University of Defense Technology, Changsha

[5] School of Earth Sciences and Engineering, Hohai University, Nanjing

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 11期

关键词：

3D laser SLAM; global descriptor; loop closure detection; spherical harmonic transforms; spherical harmonics;

D O I：

10.3724/SP.J.1089.2023.19748

中图分类号：

学科分类号：

摘要：

Loop closure detection is the core of 3D laser-based SLAM to realize autonomous positioning and naviga-tion. Aiming at the problems of high complexity and low accuracy in current loop closure detection, a spherical harmonic energy with rotation invariance (SHE) and a spherical harmonic energy with the Z-axis rotation invariance (SHZE) are first proposed in this paper. Combining the advantages of SHE and SHZE via a “two-step search”, a novel loop closure detection algorithm for 3D laser-based SLAM, denoted as SH-LCD, is proposed. SH-LCD not only improves the richness of information extraction, but also reduces the computation complexity of loop closure detection, thus characterized by strong feature extraction ability and universality. Extensive loop closure detection evaluations on benchmark datasets including KITTI, NCLT, and Complex Urban, demonstrate that the detection accuracy of SH-LCD significantly outperforms the current state-of-the-art methods including Scan Context, M2DP, OverlapNet, etc. In addition, the efficiency of SH-LCD is high, and the time for operator calculation and operator matching are about 12.0ms and 2.3ms, respectively. This meets the real-time requirements of SLAM. © 2023 Institute of Computing Technology. All rights reserved.

引用

页码：1731 / 1742

页数：11

共 34 条

[1] Hu Xiangyan, Research on closed-loop detection method of SLAM based on particle swarm optimization, (2018)
[2] Wei Shuangfeng, Pang Fan, Liu Zhenbin, Et al., Survey of LiDAR-based SLAM algorithm, Application Research of Computers, 37, 2, pp. 327-332, (2020)
[3] Steder B, Ruhnke M, Grzonka S, Et al., Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1249-1255, (2011)
[4] Steder B, Rusu R B, Konolige K, Et al., NARF: 3D range image features for object recognition
[5] Rusu R B, Blodow N, Marton Z C, Et al., Aligning point cloud views using persistent feature histograms, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384-3391, (2008)
[6] Salti S, Tombari F, Di Stefano L., SHOT: unique signatures of histograms for surface and texture description, Computer Vision and Image Understanding, 125, pp. 251-264, (2014)
[7] Belongie S, Malik J, Puzicha J., Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 4, pp. 509-522, (2002)
[8] Johnson A E, Hebert M., Using spin images for efficient object recognition in cluttered 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 5, pp. 433-449, (1999)
[9] Rusu R B, Bradski G, Thibaux R, Et al., Fast 3D recognition and pose using the viewpoint feature histogram, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155-2162, (2010)
[10] Rusu R B, Blodow N, Beetz M., Fast point feature histograms (FPFH) for 3D registration, Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3212-3217, (2009)

← 1 2 3 4 →