基于多平面分割和矩阵变换的航摄边坡点云滤波算法

An Aerial Photography Slope Point Cloud Filtering Algorithm Based on Multi-Plane Segmentation and Matrix Transformations

点云滤波是实现地面点与非地面点分离,获取最真实地面点云的重要处理手段,是进行公路边坡点云灾害识别的基础.为了克服传统点云滤波算法在边坡场景下处理速度慢、结果精度低和误差大等问题,提出了基于多平面分割和矩阵变换的航摄边坡点云滤波算法.该方法首先利用基于曲率的区域生长算法对边坡进行多平面分割,得到多个边坡子点云;其次拟合得到边坡子点云平面模型,再利用旋转矩阵将边坡子点云进行空间转换至水平面;然后通过模拟布料下沉并设置距离阈值分离出非地面点;最后再次利用旋转矩阵的逆矩阵进行空间位置还原,进而得到滤波后的边坡点云.利用精细贴近仿地航线设计方法获取高精度点云模型以在不同的边坡场景下进行算法测试,并与其他传统滤波算法结果进行对比,结果表明:在所有的试验中,本文算法的总误差分别为7.11%、4.15%、1.45%、4.41%,在所有测试算法中最小;Kappa系数分别为0.77、0.90、0.96、0.90,在所有测试算法中最大.本文提出的算法在面对地形和植被情况复杂的边坡情景下,有着较高的准确性以及较强的适用性,为公路边坡点云滤波提供了新的解决方案.

Point cloud filtering is a crucial processing technique for separating ground points from non-ground points and obtaining the most accurate ground point cloud.It serves as the foundation for landslide identification in highway slope point clouds.To address issues such as slow processing speed,low result accuracy,and high error rates encountered by traditional point cloud filtering algorithms in slope scenarios,an airborne slope point cloud filtering algorithm based on multi-plane segmentation and matrix transformation is proposed.This method initially employs a region growing algorithm based on curvature for multi-plane segmentation of the slopes,resulting in multiple sub-point clouds of the slopes.Subsequently,it fits plane models for these sub-point clouds and uses rotation matrices to spatially transform them onto a horizontal plane.Non-ground points are separated by simulating fabric settling with a distance threshold.Finally,the inverse of the rotation matrix is applied for spatial position restoration,yielding the filtered slope point cloud.High-precision point cloud models are obtained using a fine approximation flight route design method for algorithm testing in various slope scenarios.Results are compared with those of other traditional filtering algorithms,demonstrating that the algorithm in this paper outperforms the others with total errors of 7.11%,4.15%,1.45%,and 4.41%in all experiments,respectively.Furthermore,the Kappa coefficient values are 0.77,0.90,0.96,and 0.90,all of which are the highest among all tested algorithms.The proposed algorithm exhibits high accuracy and applicability,particularly in complex slope scenarios characterized by varying terrains and vegetation cover.It offers a new solution for point cloud filtering in highway slope applications.