An improved region growing algorithm in 3D laser point cloud identification of rock mass structural plane
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摘要:
交错分布的结构面构成了岩体中的薄弱部位，准确高效的岩体结构面识别和特征信息提取可为岩体稳定性评价提供重要依据。三维激光扫描技术可以极大地提高结构面勘测效率和精度，但目前主流的点云分析算法存在结构面边缘识别模糊、点云分割准确性不能满足结构面特征信息提取精度等问题。因此，考虑岩体结构面点云位置与其邻域的空间关系，利用KD-tree数据结构进行最邻近搜索的体素下采样，在稳健随机Hough变换的基础上改进了区域生长算法，通过多特征值对区域生长分割参数进行修正，依据点云法向量差值和特征终值进行结构面分割，实现了结构面产状、间距、延展度信息的提取。研究结果表明：与传统的主成分分析法和随机抽样一致法相比，在室内块体模型组成的24个结构面中，该方法在相同区域具有更高的识别率和准确率，既能在复杂变化的平面区域保证数据的完整识别，也能在平面的尖锐位置较好地分割边缘点云。利用该方法可以将24个结构面分为6组，并在识别数据中获取对应的结构面特征信息，与实际测量结果相比，角度信息误差约为1°，距离信息误差1 cm以内。利用该方法在长江干流蟒蛇寨斜坡岩体中成功识别出3组结构面同时计算各组结构面间距与延展度信息，并采用赤平投影图分析不同结构面组对斜坡稳定性的影响。所提出的方法在室内模型及现场斜坡验证效果良好，可以为岩体结构面识别分割提供稳定且有效的技术支撑。
Abstract:The rock mass structural plane constitutes the weakest part of the rock mass. Accurate and efficient identification of rock mass structural plane and extraction of characteristic information can provide an important basis for the rock mass stability evaluation. 3D laser scanning technology can greatly improve the efficiency and accuracy of structural surface survey; however, the current mainstream point cloud analysis algorithms exist the problems that the edge recognition of structural surfaces is blurred and the accuracy of point cloud segmentation cannot meet the accuracy of structural surface feature information extraction. Considering the spatial relationship between the position of the point cloud of the rock mass structural plane and its neighborhood, the region growth segmentation parameters were corrected by multiple eigenvalues. The KD-tree data structure was used to perform the nearest neighbor search. The voxel was sampled, and the structural plane was segmented to realize the extraction of the structure plane occurrence, spacing, and extension information, based on the normal vector difference of the point cloud and the characteristic final value. The effectiveness of this method in structural plane identification was also verified by indoor models. The results show that compared with the traditional Principal Component Analysis method and Random Sample Consensus method, this method has a higher recognition rate and accuracy in the same area among the 24 structural planes composed of indoor block models. It can not only ensure the complete recognition of data in the complex and changing plane area, but also better segment the edge points in the sharp position of the plane. Using this method, 24 structural planes can be divided into 6 groups, and the corresponding structural plane feature information can be obtained. Compared with the actual measurement results, the angle information error is approximately 1°, and the distance information error is within 1 cm. This method identified three groups of structural planes in the Mangshezhai slope rock mass successfully in the main stream of the Yangtze River. The method proposed in this study has a good verification effect on indoor model and field slope, which can provide robust and effective technical support for the identification and segmentation of rock mass structural plane.
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表 1 不同方法在不同结构面的识别率和识别准确率
Table 1. The comparison of the recognition rate and accuracy in the different methods
结构面 总点数 未识别点数 错误识别点数 识别率/% 识别准确率/% RSD PCA Hough RSD PCA Hough RSD PCA Hough RSD PCA Hough J1 6 596 1 047 955 408 37 73 13 84.1 84.6 93.7 99.4 98.9 99.8 J2 5 726 936 822 397 6 10 3 83.6 85.7 93.3 99.9 99.8 100 J3 7 543 1 911 1 925 955 40 56 20 74.7 73.7 85.0 99.3 99.3 99.7 J4 3 624 260 519 255 15 122 19 92.8 82.6 93.0 99.6 96.6 99.5 J5 4 438 321 605 324 10 249 14 92.8 81.0 92.6 99.8 94.4 99.7 J6 3 414 520 819 489 12 300 24 84.8 67.8 85.4 99.6 91.2 99.3 J7 1 741 230 406 241 0 0 0 86.8 77.4 86.9 100 100 100 J8 1 994 215 439 222 4 22 10 89.2 77.7 89.3 99.8 98.9 99.5 J9 2 180 223 370 204 0 303 2 89.8 70.0 91.3 100 86.1 99.9 J10 6 717 1 769 1 523 829 24 44 1 73.7 76.9 87.7 99.5 99.4 100 J11 6 680 1 551 1 423 781 39 76 8 76.8 77.7 88.3 99.3 98.9 99.9 J12 7 362 1 376 1 382 707 35 85 10 81.3 80.3 90.5 99.4 98.8 99.9 J13 7 223 1 133 1 108 891 0 0 0 84.3 84.9 87.8 100 100 100 J14 6 378 1 141 973 719 0 0 0 82.1 84.9 89.0 100 100 100 J15 7 602 2 155 2 080 1 032 51 93 12 71.7 71.6 83.7 99.1 98.8 99.8 J16 1 000 119 384 136 7 29 12 88.1 59.8 86.7 99.2 97.1 98.8 J17 1 487 232 564 238 5 44 15 84.4 59.7 83.3 99.6 97.1 99.0 J18 1 243 129 354 121 0 0 0 89.6 72.5 91.0 100 100 100 J19 1 698 551 960 367 0 2 0 67.6 43.4 72.9 100 99.9 100 J20 867 159 491 166 0 23 0 81.7 42.2 81.7 100 97.4 100 J21 1 077 170 423 178 0 129 4 84.2 48.9 83.2 100 88.0 99.6 J22 1 543 329 689 305 0 0 0 78.7 55.5 81.2 100 100 100 J23 1 395 224 501 206 0 0 0 83.9 65.2 86.0 100 100 100 J24 1 052 182 360 169 0 32 0 82.7 63.3 85.0 100 97.0 100 
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表 2 不同结构面分组及参数统计
Table 2. Different structural plane groups and parameter statistics
分组 结构面 模型实测 模型识别 平均产状 产状分布 平均间距/cm 平均延展度/cm 产状分布 平均间距/cm 平均延展度/cm JⅠ J1—J3、J15、J19 180°～200°∠88°～90° 31.5 67.5 181°～202°∠88°～90° 30.3～32.7 68.4 190.1°∠89.4° JⅡ J10—J14、J18、J23 150°～170°∠89°～90° 12.2 71.1 149°～170°∠89°～90° 10.8～13.1 71.3 165.7°∠89.7° JⅢ J17、J22 120°～140°∠89°～90° 20.4 29.6 120°～141°∠89°～90° 18.7～21.5 29.4 130.9°∠89.4° JⅣ J4—J6、J20 90°～110°∠90° 11.6 43.3 90°～110°∠89°～90° 10.0～12.7 43.5 100.9°∠89.9° JⅤ J7—J9、J24 60°～80°∠90° 3.3 15.2 61°～80°∠90° 3.0～3.4 15.4 60.9°∠89.9° JⅥ J21、J16 30°～50°∠89°～90° 9.2 16.4 30°～50°∠89°～90° 8.7～9.8 16.3 41.9°∠89.5° CⅠ C1 187.0°∠1.0° 187.0°∠1.1°
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