滑坡表面位移的无人机航测点云比对监测方法

doi:10.13474/j.cnki.11-2246.2023.0127

测绘通报 ›› 2023, Vol. 0 ›› Issue (5): 1-8.doi: 10.13474/j.cnki.11-2246.2023.0127

• 滑坡监测与分析 • 下一篇

滑坡表面位移的无人机航测点云比对监测方法

孟永东^1,2, 袁昌纬², 田斌^1,2, 蔡征龙^1,2, 张伟杰²

1. 三峡大学湖北长江三峡滑坡国家野外科学观测研究站, 湖北宜昌 443002;
2. 三峡大学水利与环境学院, 湖北宜昌 443002

收稿日期:2022-07-23 修回日期:2023-04-04 发布日期:2023-05-31
通讯作者: 田斌。E-mail:eudiltb@ctgu.edu.cn
作者简介:孟永东(1976-),男,博士,教授,研究方向为水工结构工程及岩土工程。E-mail:meng@ctgu.edu.cn
基金资助:
国家自然科学基金重点项目（51939004）；国家自然科学基金联合基金重点项目（U2240221）

The method of landslide surface displacement monitoring based on UAV aerial survey point cloud comparison

MENG Yongdong^1,2, YUAN Changwei², TIAN Bin^1,2, CAI Zhenglong^1,2, ZHANG Weijie²

1. National Field Observation and Research Station of Landslides in Three Gorges of Yangtze River, China Three Gorges University, Yichang 443002, China;
2. College of Water Conservancy and Environment, China Three Gorges University, Yichang 443002, China

Received:2022-07-23 Revised:2023-04-04 Published:2023-05-31

摘要/Abstract

摘要： 滑坡单点式位移监测难以反映滑坡的整体位移状态，采用多尺度模型到模型的点云比对算法（M3C2）可实现对滑坡的面域监测。本文首先通过对无人机采集的影像进行运动恢复结构（SfM）分析，还原滑坡三维点云模型；然后利用点云比对算法对两期点云数据进行处理，以色值大小体现滑坡区域的位移，进而识别滑坡表面位移变化；最后将该方法运用于实际边坡的表面位移监测。试验结果表明，应用M3C2算法可成功识别边坡变动区域，捕捉到1 cm的水平或垂直位移变化，能直观反映滑坡面域的变形状况。该方法适用于复杂地形条件下的滑坡位移整体监测，识别精度达到厘米级，性能优于两云直接比对算法（C2C）。

关键词: 滑坡, 表面位移监测, 无人机, M3C2, 摄影测量

Abstract: The M3C2 (multiscale model-to-model cloud comparison) algorithm can be used to achieve surface monitoring of landslides while the single-point displacement monitoring of landslides cannot reflect the overall displacement status of the landslide. By analyzing the images collected by UAV through SfM (structure from motion), a 3D point cloud model of the landslide is reconstructed.The point cloud comparison algorithm is then used to process the two sets of point cloud data, and the displacement of the landslide area is represented by color and size to identify the surface displacement changes of the landslide. The application of this method in the surface displacement monitoring of actual slopes shows that the M3C2 algorithm can successfully identify the slope change area, capture horizontal/vertical displacement changes of 1 cm, and intuitively reflect the deformation of the landslide surface. This method is suitable for overall monitoring of landslide displacement under complex terrain conditions with an identification accuracy of centimeter-level, and its performance is better than the C2C (direct cloud-to-cloud comparison with closest point technique) algorithm.

Key words: landslide, surface displacement monitoring, UAV, M3C2, photogrammetry

中图分类号:

孟永东, 袁昌纬, 田斌, 蔡征龙, 张伟杰. 滑坡表面位移的无人机航测点云比对监测方法[J]. 测绘通报, 2023, 0(5): 1-8.

MENG Yongdong, YUAN Changwei, TIAN Bin, CAI Zhenglong, ZHANG Weijie. The method of landslide surface displacement monitoring based on UAV aerial survey point cloud comparison[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 1-8.

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滑坡表面位移的无人机航测点云比对监测方法

The method of landslide surface displacement monitoring based on UAV aerial survey point cloud comparison

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