Laser odometry algorithm based on point cloud segmentation
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摘要: 针对激光里程计算法中存在的信息冗余和离散点干扰问题，本文提出了一种基于点云分割的激光里程计算法. 该方法根据机械式激光雷达的水平旋转扫描特点，对点云数据进行扫描线分割并赋予标签，提取物体的边缘点作为线特征、表面点作为面特征. 相较于传统的特征提取方法，本文算法能够有效提取具有较少标志性的特征点，同时剔除离散点. 该算法在降低计算量的同时提升了定位的精确性和鲁棒性，对于机器人导航任务有着很好的应用前景，并通过计算机仿真验证了该算法的性能，并取得了较好的实验效果.Abstract: In this paper, we propose a feature extraction and matching method based on point cloud segmentation to address the problems of information redundancy and interference of discrete points in laser odometry algorithms. By utilizing the horizontal rotation scanning characteristics of mechanical LiDAR, the point cloud data is segmented and clustered by scan lines, and edge points are extracted as line features and surface points are extracted as surface features. Compared to traditional feature extraction methods, our algorithm can effectively extract feature points with less distinctiveness and eliminate discrete points. The algorithm not only reduces the computational complexity but also improves the accuracy and robustness of positioning, which has great application prospects in robot navigation tasks. The performance of the algorithm is validated through computer simulation and achieved good experimental results.
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Key words:
- LiDAR /
- point cloud /
- segmentation /
- feature extraction /
- feature matching /
- odometer
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[1] 王朋, 郝伟龙, 倪翠, 等. 视觉SLAM方法综述[J/OL]. (2022-12-26)[2023-02-21]. 北京航空航天大学学报, 2023: 1-9. https://bhxb.buaa.edu.cn/bhzk/cn/article/doi/10.13700/j.bh.1001-5965.2022.0376 [2] 梁双, 倪晓昌, 董娇玲, 等. 基于激光雷达的SLAM算法综述[J]. 信息与电脑(理论版), 2022, 34(3): 59-61. [3] HORAUD R, HANSARD M, EVANGELIDIS G, et al. An overview of depth cameras and range scanners based on time-of-flight technologies[J]. Machine vision & applications, 2016, 27(7): 1005-1020. DOI: 10.1007/s00138-016-0784-4 [4] ELHOUSNI M, HUANG X M. A survey on 3D LiDAR localization for autonomous vehicles[C]//IEEE Intelligent Vehicles Symposium (IV), 2020. DOI: 10.1109/IV47402.2020.9304812 [5] 周治国, 曹江微, 邸顺帆. 3D激光雷达SLAM算法综述[J]. 仪器仪表学报, 2021, 42(9): 13-27. DOI: 10.19650/j.cnki.cjsi.J2107897 [6] ZHANG J, SINGH S. Low-drift and real-time Lidar odometry and mapping[J]. Autonomous robots, 2017, 41(2): 401-416. DOI: 10.1007/s10514-016-9548-2 [7] HORACHE S, DESCHAUD J-E, GOULETTE F. 3D point cloud registration with multi-scale architecture and unsupervised transfer learning[C]//International Conference on 3D Vision (3DV), 2021. DOI: 10.1109/3DV53792.2021.00142 [8] SERAFIN J, OLSON E, GRISETTI G. Fast and robust 3D feature extraction from sparse point clouds[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. DOI: 10.1109/IROS.2016.7759604 [9] ABEDINI A, HAHN M, SAMADZADEGAN F. An investigation into the registration of LIDAR intensity data and aerial images using the SIFT approach[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science Conference, 2008: 169-175. [10] ARSHAD S, KIM G-W. Role of deep learning in loop closure detection for visual and lidar slam: a survey[J]. Sensors, 2021, 21(4): 1243. DOI: 10.3390/s21041243 [11] 方国润. 三维点云分割研究综述[J]. 计量与测试技术, 2021, 48(7): 52-55. DOI: 10.15988/j.cnki.1004-6941.2021.7.016 [12] BOGOSLAVSKI I, STACHNISS C. Fast range image-based segmentation of sparse 3D laser scans for online operation[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016. DOI: 10.1109/IROS.2016.7759050 -
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