对渐进三角网点云数据滤波算法的改进

Improvement of point cloud filtering algorithm for the progressive TIN

针对渐进三角网滤波算法(PTD)进行拓普康LiDAR点云数据处理过程中易将地物点误判为地面点的缺陷,本文提出两种改进方法。一种是采用局部坡度拟合法对PDT算法进行改进,将点云数据按高程值与拟合坡面法求解的拟合高程值的差由小到大进行排序,将为地面点可能性更大的点优先判定,从而获取更加精确的TIN;另一种是引入薄板样条曲线(TPS)插值法,对PTD算法进行改进,将PTD中候选点判断参数改为TPS法中的弯曲能量增长值,从而减少误判。结果表明,使用以上两种改进算法,综合考虑第1类误差和第2类误差影响,在大部分地形特征下比传统PTD算法表现更优,对低矮植被、桥、斜坡等特殊地物的滤波效果更佳。

This paper analyzes the defect that the feature points are easily misjudged as ground points in the process of Topcon LiDAR point cloud data processing by progressive TIN densification algorithm(PTD),proposes two kinds of improved methods.The first method is to use the local slope fitting method to improve the PDT algorithm,and sort the point cloud data according to the difference between the elevation value and the fitting elevation value solved by the fitting slope method from small to large,the point which is more greater possibility for the ground point is determined firstly,so as to obtain a more precise tin.The second method is to use thin plate spline(TPS)interpolation to improve the PTD algorithm,change the judgment parameter of candidate point in PTD to the threshold value of bending energy in TPS,so as to reduce misjudgment.The results show that considering the influence of the first error and the second error,the two improved algorithms are better than the traditional PTD algorithm in most terrain features,and have better filtering effect on low vegetation,bridges,slopes and other special objects.