基于已知点约束的高精度煤矿巷道三维点云建模方法

马亮; 高亮; 廉博翔; 张琦; 蔺小虎; 姜之跃

doi:10.13272/j.issn.1671-251x.2024080093

基于已知点约束的高精度煤矿巷道三维点云建模方法

马亮^1,,
高亮¹,
廉博翔¹,
张琦¹,
蔺小虎^2, ,,
姜之跃²

1.
陕煤集团神木柠条塔矿业有限公司，陕西榆林　719300
2.
西安科技大学测绘科学与技术学院，陕西西安　710054

基金项目: 国家自然科学基金青年项目（42201484）；中国博士后科学基金项目（2023MD744243）；陕西省教育厅专项科研计划项目（23JK0544）。

详细信息

作者简介:
马亮（1985—），男，陕西绥德人，高级工程师，现从事矿山灾害与预防方面的工作，E-mail：1922893877@qq.com

通讯作者:
蔺小虎（1989—），男，陕西延安人，副教授，博士，主要研究方向为移动机器人智能感知与建图，E-mail：xhlin214@xust.edu.cn。

中图分类号: TD67
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出版历程
- 收稿日期: 2024-08-30
- 修回日期: 2024-11-24
- 网络出版日期: 2024-12-01
- 刊出日期: 2024-11-24

High-precision 3D point cloud modeling method for coal mine roadways based on known point constraints

1.
Shenmu Ningtiaota Coal Mining Co., Ltd., Shaanxi Coal and Chemical Industry Group Co., Ltd., Yulin 719300, China
2.
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要:
面对煤矿井下低照度、弱纹理、高粉尘等复杂条件，现有煤矿巷道三维建模方法存在成本高、时效性差和精度低等问题，提出了一种基于已知点约束的高精度煤矿巷道三维点云建模方法。首先，通过体素滤波器对激光雷达点云数据进行降采样，并对降采样后的激光雷达点云数据利用迭代最近点（ICP）匹配提取出局部点云地图，结合惯性测量单元（IMU）数据对点云数据进行畸变校正；其次，利用ICP配准局部点云地图和畸变校正后的点云地图，以提高前端配准的精度和效率，并在后端加入回环检测来提高煤矿巷道定位与建图精度；然后，通过附合导线控制测量获取煤矿巷道分段已知点坐标，为点云建模提供全局约束条件；最后，将已知点和激光雷达同时定位与建图（SLAM）确定的测站点进行联合平差计算，对测站点坐标进行校正，并进一步利用非线性优化方法校正全局点云地图坐标，从而提高三维点云建模精度。实验结果表明：该方法构建的煤矿巷道三维点云地图具有较好的全局一致性和几何结构真实性，在煤矿井下具有较高的定位与建图精度。
- 巷道建模 /
- 三维激光雷达扫描 /
- 同时定位与地图构建 /
- SLAM /
- 已知点约束 /
- 三维点云地图
Abstract:
In response to the complex conditions such as poor lighting, weak texture, and high concentrtaions of dust in underground coal mines, the existing 3D modeling methods for coal mine roadways have the disadvantages of high costs, poor timeliness, and low accuracy. A high-precision 3D point cloud modeling method for coal mine roadways based on known point constraints was proposed. The LiDAR point cloud data was downsampled by voxel filter, followed by the use of iterative closest point (ICP) matching for the downsampled LiDAR point cloud data to extract local point cloud maps. The point cloud data was then distortion-corrected using inertial measurement unit (IMU) data. ICP was utilized to align the local point cloud maps with the distortion-corrected point cloud maps, improving the accuracy and efficiency of front-end registration. Loopback detection was incorporated in the back-end to enhance the accuracy of coal mine roadway localization and mapping. The coordinates of the known points of the coal mine roadways were obtained through control measurements using connecting traverse, providing global constraints for point cloud modeling. A combined adjustment calculation was performed on the known points and the station points determined by LiDAR simultaneous localization and mapping (SLAM). The station point coordinates were corrected, and a nonlinear optimization method was further employed to adjust the global point cloud map coordinates, thereby improving the accuracy of 3D point cloud modeling. Experimental results demonstrated that the 3D point cloud map of coal mine roadways constructed by this method had high global consistency and geometric structure authenticity, achieving high localization and mapping accuracy in underground coal mines.
- roadway modeling /
- 3D LiDAR scanning /
- simultaneous localization and mapping /
- SLAM /
- known point constraints /
- 3D point cloud map

HTML全文

图 1 已知点约束的高精度煤矿巷道三维点云建模流程

Figure 1. Workflow of high-precision 3D point cloud modeling for coal mine roadways based on known point constraints

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图 2 附合导线控制测量原理

Figure 2. Principle of control measurement using connecting traverse

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图 3 实验场景

Figure 3. Experimental scene

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图 4 不同方法在实验室模拟巷道中的建图结果

Figure 4. Mapping results of different methods in laboratory simulated roadways

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图 5 添加已知点约束前后的煤矿巷道建图结果

Figure 5. Mapping results of coal mine roadways before and after incorporating known point constraints

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图 6 不同方法定位精度对比

Figure 6. Comparison of localization accuracy of different methods

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图 7 点云地图特征地物尺寸测量

Figure 7. Dimension measurement of feature objects in point cloud map

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表 1 不同方法的定位误差

Table 1 Localization errors of different methods

方法	误差类别	最大误差/m	最小误差/m	平均误差/m	相对误差/%
LeGO− LOAM	水平误差	9.253	0.025	0.708	3.86
LeGO− LOAM	竖直误差	7.933	0.032	1.684	3.31
LIO− SAM	水平误差	3.314	0.021	1.475	1.38
LIO− SAM	竖直误差	2.417	0.024	0.582	1.01
本文方法	水平误差	0.426	0.023	0.199	0.18
本文方法	竖直误差	1.044	0.026	0.455	0.45

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表 2 不同方法对应的特征地物尺寸测量误差对比

Table 2 Comparison of dimension measurement errors of feature objects using different methods

方法	特征地物	真实长度/m	测量长度/m	绝对误差/m	相对误差/%
LeGO− LOAM	1	4.600	4.356	0.244	5.30
	2	2.000	1.916	0.084	4.20
	3	0.600	0.585	0.015	2.50
	4	3.500	3.663	0.163	4.66
	5	0.900	0.923	0.023	2.56
	6	1.500	1.482	0.018	1.20
LIO− SAM	1	4.600	4.562	0.038	0.83
	2	2.000	1.986	0.014	0.70
	3	0.600	0.595	0.005	0.83
	4	3.500	3.533	0.033	0.94
	5	0.900	0.862	0.038	4.22
	6	1.500	1.490	0.010	0.67
本文方法	1	4.600	4.582	0.018	0.39
	2	2.000	1.991	0.009	0.45
	3	0.600	0.595	0.005	0.83
	4	3.500	3.487	0.013	0.37
	5	0.900	0.893	0.007	0.78
	6	1.500	1.493	0.007	0.47

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