基于无人机LiDAR仿地飞行技术的高陡边坡危岩体快速识别方法

庞鑫; 袁明; 卢渊; 杜文杰; 万道春; 李得; 丁海锋; 付晓东

doi:10.19509/j.cnki.dzkq.tb20220427

基于无人机LiDAR仿地飞行技术的高陡边坡危岩体快速识别方法

doi: 10.19509/j.cnki.dzkq.tb20220427

庞鑫^1,,
袁明²,
卢渊³,
杜文杰^{4, 5},
万道春¹,
李得¹,
丁海锋^{4, 5, ,},
付晓东^{4, 5, ,}

1.
攀钢集团矿业有限公司, 四川攀枝花 617000
2.
四川电力设计咨询有限责任公司, 成都 610000
3.
中国电力科学研究院有限公司输变电工程研究所, 北京 102401
4.
中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室, 武汉 430071
5.
中国科学院大学, 北京 100049

基金项目:

国家自然科学基金项目 52179117

湖北-国家自然科学联合基金项目 U21A20159

详细信息

作者简介:
庞鑫(1982—), 男, 高级工程师, 主要从事非煤露天矿山边坡地质灾害治理等方面的研究工作。E-mail: 36754555@qq.com

通讯作者:
丁海锋(1997—), 男, 现正攻读土木水利专业硕士学位, 主要从事边坡稳定性分析与灾变机制方面的研究工作。E-mail: dinghaifeng21@mails.ucas.ac.cn

付晓东(1986—), 男, 副研究员, 主要从事边坡地质灾害数值模拟与评估等方面的研究工作。E-mail: xdfu@whrsm.ac.cn

中图分类号: P23;P642.2;TN958.98
计量
- 文章访问数: 548
- PDF下载量: 86
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-06
- 录用日期: 2022-10-03
- 修回日期: 2022-09-28

Rapid identification method for the dangerous rock mass of a high-steep slope based on UAV LiDAR and ground imitation flight

Pang Xin^1
,,
Yuan Ming²,
Lu Yuan³,
Du Wenjie^{4, 5},
Wan Daochun¹,
Li De¹,
Ding Haifeng^{4, 5
, ,},
Fu Xiaodong^{4, 5
, ,}

1.
Panzhihua Iron and Steel Group Mining Co., Ltd., Panzhihua Sichuan 617000, China
2.
Sichuan Electric Power Design Consulting Co., Ltd., Chengdu 610000, China
3.
Power Transformer Engineering Research Institute, China Electric Power Research Institute, Beijing 102401, China
4.
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
5.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要:
我国西南地区山高谷深、斜坡高陡, 危岩落石灾害极为发育, 高陡边坡危岩落石由于高差大、坡面陡, 具有显著的突发性, 因此快速、准确、便捷地解译与识别危岩体源头成为高陡岩质边坡风险分析的首要问题。当前探测手段的进步使基于影像的地质灾害解译逐步从目视识别向人机交互式识别方向发展。其中, 无人机激光雷达(LiDAR)系统通过融合无人机载体与LiDAR测量技术的优势, 广泛应用于地质灾害调查中, 同时仿地飞行技术的引入可以使无人机LiDAR系统适应复杂的地形, 快速获取高精度、高密度的点云数据。基于以上技术对攀枝花某铁矿露天采场东侧边坡开展了无人机调查, 通过获取高精度DOM影像和三维点云模型, 量化提取点云模型中露头坡面粗糙度、倾向等危岩体几何特征参数作为DOM影像的补充材料, 提出了一套基于DOM影像和几何特征的危岩体人机交互式识别方法。通过对东侧边坡危岩体的应用表明: 提出的人机交互式识别方法通过在DOM影像的基础上叠加露头坡面几何特征, 对悬空危岩体的识别精度优于目视识别, 可以显著提高危岩体识别的效率和准确性。提出的方法通过结合创新的遥感技术, 为高陡岩质边坡危岩体识别提供了快速便捷的方案。
- 高陡边坡 /
- 危岩体 /
- 无人机LiDAR系统 /
- 仿地飞行 /
- 三维点云模型
Abstract: Objective
In Southwest China, rockfall hazards are extremely developed in high mountains and deep valley areas with high and steep slopes. Due to the large elevation difference and steep slope, the dangerous rock mass on a high-steep slope has remarkable characteristics of suddenness. Thus, a rapid, accurate and convenient interpretation and identification for the source of dangerous rock mass becomes the primary problem of risk analysis of high-steep slopes. At present, the progress of detection makes image-based geological hazard interpretation gradually develop from visual identification to human-computer interactive identification. Among them, the UAV LiDAR system is widely used in geological disaster investigation by integrating both advantages of UAV carrier and LiDAR measurement technology, while the introduction of ground imitation flight technology can make the UAV LiDAR system adapt to complex terrain, obtaining high-precision and high-density point cloud data.
Methods
On this basis, a UAV survey was carried out on the East side slope of open-pit mine, and a high-precision DOM image and 3D point cloud model were obtained by processing the UAV survey data. As the supplementary materials of the DOM image, the geometric feature parameters of outcrop, including surface roughness and dip, are quantitatively extracted from the point cloud model. On this basis, a set of human-computer interactive identification for dangerous rock masses based on DOM images and geometric features is proposed.
Results
The application to the East side slope of open-pit mine shows that by superimposing outcrop slope geometric features based on DOM images, the proposed human-computer interactive identification method can significantly improve the efficiency and accuracy of identification, and the identification of overhanging dangerous rock mass is much more robust than visual ones.
Conclusion
By combining innovative remote sensing technologies, the proposed method provides a fast and convenient solution for the identification of dangerous rock masses on high and steep rocky slopes.
- high-steep slope /
- dangerous rock mass /
- UAV LiDAR system /
- ground imitation flight /
- 3D point cloud model
注释:

(所有作者声明不存在利益冲突)

HTML全文

图 1 无人机遥感技术与传统地质勘察手段的结合

Figure 1. Combination of UAV remote sensing technology and traditional geological survey

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图 2 仿地飞行示意图

Figure 2. Sketch map of the ground imitation flight

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图 3 技术路线图

Figure 3. Technical route of the study

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图 4 研究现场的总体情况

a.四川地形图及研究区域位置；b.F₁₀₆断层；c.失稳前研究区域DOM；d.破坏前影像；e.破坏后影像

Figure 4. Overall situation of the study site

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图 5 无人机设备与仿地航线规划

a.DJI M300 RTK搭载AlphaAir 450 LiDAR; b.仿地飞行航线

Figure 5. UAV equipment and route planning of ground imitation flight

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图 6 基于CSF方法的地面点和非地面点分离

Figure 6. Separation of ground and off-ground points based on CSF

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图 7 基于DOM和点云透视投影的危岩体初步识别

a.DOM(正视图)；b.DOM(侧视图)；c.三维点云透视投影(正视图)；d.三维点云透视投影(侧视图)

Figure 7. Preliminary recognition of dangerous rock masses based on DOM and perspective projection of point cloud model

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图 8 剖面分析

Figure 8. Profile analysis

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图 9 采场东侧边坡露头研究区域

Figure 9. Study area of slope outcrop on East side of stope

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图 10 结构面点簇分布

Figure 10. Point cluster distribution of structural plane

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图 11 表面粗糙度指数提取

Figure 11. Extraction of the surface roughness index

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图 12 露头坡面倾向

Figure 12. Dip of the outcrop

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图 13 突出岩体悬空面提取(倾向)

Figure 13. Extraction of the suspended surface of the outburst rock mass

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图 14 基于DOM和几何特征参数的人机交互式识别

Figure 14. Human-computer interactive recognition based on DOM image and geometric features

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表 1 3类探测手段的对比

Table 1. Comparison of three detection approachs

探测技术	精度	速度	成本	应用范围
UAV激光雷达	非常高/cm	很快	高	大范围/102 km²
UAV摄影测量	较高/cm~m	较快	较高	较大范围/10 km²
遥感卫星	低	很快	较高	全球范围

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表 2 CSF参数

Table 2. CSF parameters

参数	分辨率/m	最大迭代步	分类阈值
数值	0.2	500	0.2

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表 3 4组结构面产状信息

Table 3. Four sets of discontinuities and their characteristics

点簇	倾向/(°)	倾角/(°)	密度/(个·cm^-3)	产状点云占比/%
J₁	208.384 3	38.734 1	3.289 8	68.46
J₂	358.556 0	88.600 1	0.121 1	7.94
J₃	10.107 5	86.614 7	0.120 4	7.38
J₄	340.624 3	86.044 1	0.095 9	6.15

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