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摘要:
针对移动机器人在未知环境感知过程中对三维点云快速聚类分割的需求，提出一种基于图像信息约束的三维激光点云聚类方法。首先通过点云预处理获取有效的三维环境信息，采用RANSAC方法进行地面点云的分割剔除。其次传感器数据在完成时空配准后引入YOLOv5目标检测算法，对三维点云K-means聚类算法进行改进，利用二维图像目标物的检测框范围约束三维点云，减少非目标物的干扰；基于图像检测信息实现点云聚类算法的参数初始化；采用类内异点剔除法优化聚类结果。最后搭建移动机器人硬件平台，对箱体进行测试，实验结果表明，本文方法的聚类准确率和聚类时间分别为86.96%和23 ms，可用于移动机器人导航避障、自主搬运等领域。
Abstract:Aiming at the requirement of fast clustering and segmentation of 3D point clouds for mobile robots in the process of perception of unknown environments, a 3D laser point cloud clustering method based on image information constraints is proposed. Firstly, the effective 3D environment information is obtained through point cloud preprocessing, and the RANSAC method is used to segment and eliminate the ground point cloud. Secondly, the sensor data is introduced into the YOLOv5 target detection algorithm after completing the spatiotemporal registration, and the K-means clustering algorithm of the 3D point cloud is improved. The detection frame range of the 2D image target is used to constrain the 3D point cloud and reduce the interference of non-target objects. The parameter initialization of the point cloud clustering algorithm is realized based on the image detection information. The clustering results are optimized by the intra-class outlier elimination method. Finally, the mobile robot hardware platform is built, and the box is tested. The experimental results show that the clustering accuracy and clustering time of the method in this paper are 86.96% and 23 ms, respectively, which can be used in mobile robot navigation and obstacle avoidance, autonomous handling, and other fields.
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Key words:
- moving robot /
- LiDAR /
- target detection /
- point cloud clustering
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图 1 基于图像信息约束的三维激光点云聚类算法流程图
Figure 1. Flow chart of 3D laser point cloud clustering algorithm constrained by image information
图 2 点云数据预处理。 (a) 处理前； (b) 处理后
Figure 2. Preprocessing of point cloud data. (a) Before processing; (b) After processing
图 3 地面分割。(a) 地面点； (b) 非地面点
Figure 3. Ground segmentation. (a) Groud points; (b) Non-groud points
图 10 激光雷达与相机标定。(a) 标定前； (b) 标定后
Figure 10. LiDAR and camera calibration. (a) Before calibration; (b) After calibration
图 11 多种算法聚类结果。 (a) DBSCAN； (b) Euclidean Clustering； (c) K-means++； (d) My-method
Figure 11. Clustering results of multiple algorithms. (a) DBSCAN; (b) Euclidean Clustering; (c) K-means++; (d) My-method
表 1 内参标定结果
Table 1. Calibration results of internal parameters
fx fy cx cy k1 k2 p1 p2 K 657.58 660.12 296.12 246.35 — — — — D — — — — 0.238809 −0.643802 0.001786 −0.024125 
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表 2 外参标定结果
Table 2. Calibration results of external parameters
x/mm y/mm z/mm Roll/rad Pitch/rad Yaw/rad T 59.94 52.76 −14.46 −1.540 0.031 −1.581
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表 3 分布间距对算法影响
Table 3. Affects of distribution spacing on the algorithm
Distribution spacing/cm My-method K-means K-means++ Euclidean Clustering DBSCAN $ \omega $ $ \eta $ $ \omega $ $ \eta $ $ \omega $ $ \eta $ $ \omega $ $ \eta $ $ \omega $ $ \eta $ 2 ✓ — ✓ 0.44 ✓ 0.72 ✗ — ✗ — 5 ✓ — ✓ 0.46 ✓ 0.70 ✗ — ✗ — 10 ✓ — ✓ 0.72 ✓ 0.88 ✗ — ✓ — 15 ✓ — ✓ 0.62 ✓ 0.74 ✓ — ✓ — 20 ✓ — ✓ 0.54 ✓ 0.70 ✓ — ✓ —
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表 4 多种算法性能对比
Table 4. Performance comparison of multiple algorithms
Algorithm Number of correct
divisions/numberClustering
accuracy/%Average time
spent/msAverage number of
iterations/numberDBSCAN 258 70.11 3.625 — Euclidean Clustering 262 71.20 2.517 — K-means 210 57.07 1.951 12 K-means++ 222 60.33 3.373 10 My-method 320 86.96 1.106 6
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