基于无人机的道路点云数据分割提取算法

Road Point Cloud Data Segmentation and Extraction Algorithm Based on UAV

针对机载LiDAR获得道路的数据信息精确度低问题,提出基于无人机的低空扫描三维点云数据,动态拟合提取分割道路信息的算法.首先使用主成分分析法获得道路点数据的法向量,之后将高程信息和法向量信息结合,利用聚类算法获得道路的高程和法向量的范围,提取道路点云数据;其次利用多项式拟合对道路数据进行数学建模;然后通过动态多项式拟合提取出所有路面数据和路面上的资产以及行人车辆数据;最后使用区域生长算法对路面上的资产以及行人车辆数据进行分割.实验表明算法对道路上的遮挡物有很强的抗干扰能力,可以将路面提取出来并将路面上的数据分割进行分割,将本文算法与区域生长算法进行对比,本文算法对路面数据更加敏感.

Aiming at the low accuracy of road data information obtained by airborne LiDAR,an algorithm for extracting and segmenting road information by dynamic fitting based on low-altitude scanning three-dimensional point cloud data of UAV(Unmanned Aerial Vehicle)is proposed.Firstly,the principal component analysis algorithm is used to obtain the normal vector of road point data.Then,combining elevation information with normal vector information,the range of road elevation and normal vector is obtained by clustering algorithm,and the road point cloud data is extracted afterwards by range.Secondly,polynomial fitting is used to model the road data.Then the dynamic polynomial fitting is used to extract the data of the whole section of road surface,assets on the road,pedestrian and vehicle data.Finally,the region growth algorithm is used to segment the assets and pedestrian vehicle data on the road surface.The experiment shows that the proposed algorithm has a strong anti-interference ability to block objects on the road.It can extract the road surface and segment the data on the road surface.The proposed algorithm in this study is more sensitive to the road surface data by comparing with the region growth algorithm.