Extraction of Road Point Cloud in Open Pit Based on Multi-feature Constraints

doi:10.12068/j.issn.1005-3026.2024.09.014

Abstract

Abstract:

Aiming at the problem that road point cloud data in open pit is difficult to be accurately extracted through point cloud features such as normal vector and kerb， a method of road point cloud extraction in open pit with multi?feature constraints was proposed. Taking the laser point cloud in the open pit of Qianshan limestone mine in Liaoyang City as the data source， the original data was downsampled firstly， and then the training set and verification set were made and divided based on the five kinds of point cloud features including single point RGB information， neighborhood RGB information， neighborhood height difference， neighborhood roughness， and reflection intensity. The road point cloud extraction model was constructed and optimized using the random forest algorithm. Furthermore， European clustering algorithm was introduced to improve the road point cloud extraction model. Finally， the road point cloud extraction results were evaluated in open pit. The results show that the proposed method can effectively and accurately extract the road point cloud data in open pit in real time.

Key words: open pit, road point cloud, point cloud characteristic information, random forest algorithm, European clustering algorithm

CLC Number:

P 231

Ya-chun MAO, Zhe-xi YANG, Wang CAO, QI Ji. Extraction of Road Point Cloud in Open Pit Based on Multi-feature Constraints[J]. Journal of Northeastern University(Natural Science), 2024, 45(9): 1326-1333.

Figures/Tables 13

Fig.1 Qianshan limestone mine，Liaoyang City

Fig.2 DJI M300 drone and Zenith L1 three?dimensional laser scanner

Fig.3 Flow chart for point cloud extraction of open?pit mine roadways

Fig.4 Data set partitioning

Fig.5 Two types of point cloud characteristic values

Fig.6 Random forest Bagging idea

Fig.7 Parameter optimization

Table 1 Overall score of optimal parameters

参数名	最优参数	Accuracy	F1	Oob
决策树划分标准	Gini	0.962?773	0.961?037	0.981?782
最佳决策树数量	37	0.963?026	0.961?533	0.985?605
决策树最大深度	10	0.964?306	0.962?922	0.980?794
分割内部节点所需的最小样本数	16	0.964?448	0.963?075	0.980?963
叶节点所需的最小样本数	2	0.963?453	0.961?997	0.981?033
最大特征数	5	0.963?879	0.962?459	0.982?851

Fig.8 Classification result of point cloud in open pit

Fig.9 Road point cloud after test set classification

Fig.10 Visual interpretation of road point cloud in open pit

Table 2 Extraction errors of road point cloud in open pit

类别	正确点个数	错误点个数	总数	误差/%
道路点	90?854	1?622	92?476	1.754
非道路点	1 902 353	6 793	1?909?146	0.356
总数	1 993 207	8 415	2?001?622	0.420

Table 3 Judgment errors of road point cloud in open pit

类别	道路判定误差/%
正确提取	93.043
错误提取	6.957

References 20

1	Tong X H， Liu X F， Chen P，et al.Integration of UAV‑based photogrammetry and terrestrial laser scanning for the three‑dimensional mapping and monitoring of open‑pit mine areas［J］.Remote Sensing，2015，7（6）：6635-6662.
2	Ma L F， Li Y， Li J，et al.Mobile laser scanned point‑clouds for road object detection and extraction：a review［J］.Remote Sensing，2018，10（10）：1531.
3	Huang W X， Liang H W， Lin L L，et al.A fast point cloud ground segmentation approach based on coarse‑to‑fine Markov random field［J］.IEEE Transactions on Intelligent Transportation Systems，2022，23（7）：7841-7854.
4	Mi X X， Yang B S， Dong Z，et al.Automated 3D road boundary extraction and vectorization using MLS point clouds［J］.IEEE Transactions on Intelligent Transportation Systems，2022，23（6）：5287-5297.
5	Li K， Shao J J， Guo D.A multi‑feature search window method for road boundary detection based on LIDAR data［J］.Sensors，2019，19（7）：1551.
6	Lu X W， Ai Y F， Tian B.Real‑time mine road boundary detection and tracking for autonomous truck［J］.Sensors，2020，20（4）：1121.
7	Kumar P， Lewis P， McElhinney C P，et al.Snake energy analysis and result validation for a mobile laser scanning data‑based automated road edge extraction algorithm［J］.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2017，10（2）：763-773.
8	Xiong H Y， Zhu T H， Liu Y，et al.Road‑model‑based road boundary extraction for high definition map via LIDAR［J］.IEEE Transactions on Intelligent Transportation Systems，2022，23（10）：18456-18465.
9	程效军，郭王，李泉，等.基于强度与颜色信息的地面LiDAR点云联合分类方法［J］.中国激光，2017，44（10）：273-280.
	Cheng Xiao‑jun， Guo Wang， Li Quan，et al.Joint classification method for terrestrial LiDAR point cloud based on intensity and color information［J］.Chinese Journal of Lasers，2017，44（10）：273-280.
10	Yadav M.A multi‑constraint combined method for road extraction from airborne laser scanning data［J］.Measurement，2021，186：110077.
11	Grilli E， Remondino F.Machine learning generalisation across different 3D architectural heritage［J］.ISPRS International Journal of Geo‑information，2020，9（6）：379.
12	Biçici S， Zeybek M.Effectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle‑based point cloud［J］.Transportation Research Record：Journal of the Transportation Research Board，2021，2675（12）：401-418.
13	Ferraz A， Mallet C， Chehata N.Large scale road network extraction in forested moutainous areas using airborne laser scanning data［C］//2014 IEEE Geoscience and Remote Sensing Symposium.Quebec City：IEEE，2014：4315-4318.
14	Ferraz A， Mallet C， Chehata N.Large‑scale road detection in forested mountainous areas using airborne topographic lidar data［J］.ISPRS Journal of Photogrammetry and Remote Sensing，2016，112：23-36.
15	Deng L Y， Yang M， Liang Z D，et al.Fusing geometrical and visual information via superpoints for the semantic segmentation of 3D road scenes［J］.Tsinghua Science and Technology，2020，25（4）：498-507.
16	Ma L F， Li Y， Li J，et al.BoundaryNet：extraction and completion of road boundaries with deep learning using mobile laser scanning point clouds and satellite imagery［J］.IEEE Transactions on Intelligent Transportation Systems，2022，23（6）：5638-5654.
17	张爱武，刘路路，张希珍.道路三维点云多特征卷积神经网络语义分割方法［J］.中国激光，2020，47（4）：269-277.
	Zhang Ai‑wu， Liu Lu‑lu， Zhang Xi‑zhen.Multi‑feature 3D road point cloud semantic segmentation method based on convolutional neural network［J］.Chinese Journal of Lasers，2020，47（4）：269-277.
18	李健，姚亮.融合多特征深度学习的地面激光点云语义分割［J］.测绘科学，2021，46（3）：133-139，162.
	Li Jian， Yao Liang.Ground laser point cloud semantic segmentation based on multi‑feature deep learning［J］.Science of Surveying and Mapping，2021，46（3）：133-139，162.
19	Breiman L.Random forests［J］.Machine Language，2001，45（1）：5-32.
20	毛亚纯，伏雨文，曹旺，等.基于无人机点云数据的露天采场矿车提取方法［J］.东北大学学报（自然科学版），2021，42（6）：842-848，863.
	Mao Ya‑chun， Fu Yu‑wen， Cao Wang，et al.Extraction method of open pit mine car based on UAV point cloud data［J］.Journal of Northeastern University （Natural Science），2021，42（6）：842-848，863.

[1]	MAO Ya-chun， FU Yu-wen， CAO Wang， ZHAO Zhan-guo. Extraction Method of Open Pit Mine Car Based on UAV Point Cloud Data [J]. Journal of Northeastern University(Natural Science), 2021, 42(6): 842-849.
[2]	WEI Lian-huan， LIU Shan-jun， YANG Tian-hong， ZHAO Ye. Collaborative Monitoring of Open Pit Mine Induced Large Scale Landslide with LiDAR DEM and Time Series SAR Data [J]. Journal of Northeastern University(Natural Science), 2021, 42(12): 1753-1760.
[3]	MAO Ya-chun， CAO Wang， ZHAO Zhan-guo， XU Mao-lin. Error Correction and Deformation Analysis of GB-SAR Monitoring Based on the Multiple Regression Model [J]. Journal of Northeastern University Natural Science, 2020, 41(1): 125-130.
[4]	WANG Qing， GU Xiao-wei， XU Xiao-chuan. Influence of Cutoff Grade on Optimum Pit Limit and Total Profit [J]. Journal of Northeastern University Natural Science, 2018, 39(8): 1187-1191.
[5]	XU Xiao-chuan， GU Xiao-wei， WANG Qing， ZHANG Wei-feng. Ultimate Metal Pit Optimization with Various Wall Slope [J]. Journal of Northeastern University Natural Science, 2018, 39(5): 716-721.
[6]	XU Xiao-chuan， GU Xiao-wei， WANG Qing ， LIU Jian-ping. Influence of Multi-factors on Ultimate Pit Design of Open Pit Mine [J]. Journal of Northeastern University Natural Science, 2018, 39(4): 570-574.
[7]	GU Xiao-wei， ZHAO Yun-qi， WANG Qing， XU Xiao-chuan. Relationship Between Cut-off Grade and Ultimate Pit of Open Pit Mine [J]. Journal of Northeastern University Natural Science, 2018, 39(4): 575-578.
[8]	XU Xiao-chuan， GU Xiao-wei， WANG Qing， LIU Jian-ping. Optimizing the Whole Boundary of Open Pit Mining Areas with Restrictions [J]. Journal of Northeastern University Natural Science, 2016, 37(1): 79-84.
[9]	LI Hai-ying， REN Feng-yu， YAN Guo-fu， ZHAO Yun-feng. Control Method of Rock Caving Hazard During Transition from Open Pit to Underground Mining [J]. Journal of Northeastern University Natural Science, 2015, 36(3): 419-423.
[10]	XU Xiaochuan， GU Xiaowei， WANG Qing， LIU Jianping. Improved Optimization Algorithm of Ultimate Pit for Open Pit Metal Mine [J]. Journal of Northeastern University Natural Science, 2014, 35(8): 1195-1198.