基于车载激光点云的高精地图矢量化成图

针对车载激光点云中对各特征物提取结果后矢量化成图时的自动化问题,本文基于双方向积分法实现了边缘检测及矢量化成图,旨在保证特征物基本特征的同时,也保证点云的绝对精度。将输入的特征点云进行离群点过滤,以保证外包框算法特征点云的准确度;将三维点云按照外包框算法投影至最优平面,为后续沿各方向积分提供输入;利用八邻域KD-tree算法求出样本特征点云的均值邻域半径,依据邻域半径对各方向积分提供积分域中的微分元;根据提供的微分元沿各方向进行积分,在该积分元内找到距平面最值的最优解;按照积分结果构建点云索引,并根据点云特征构建模型,最终得到高精地图的矢量化点云。试验证明了该方法在处理实际问题时的可行性。

High-precision map vectorization mapping based on vehicle borne laser point cloud

According to the results of feature extraction in vehicle laser point cloud, the requirement of automatic vectorization is put forward. In this paper, a method of edge detection and vectorization based on bi-directional integration is proposed. Experiments results show that:The method is feasible in dealing with practical problems. Outlier filtering is applied to the input feature point cloud to ensure the accuracy of the feature point cloud of the outsourcing box algorithm. Three-dimensional point cloud is projected to the optimal plane according to the bounding box algorithm to provide input for the subsequent integration along all directions. According to the eight neighborhood KD-tree algorithm, the mean neighborhood radius of the sample feature point cloud is calculated. The purpose is to provide the differential elements in the integration domain for the integration in all directions according to the neighborhood radius. According to the provided differential element, we integrate in all directions, and find the optimal solution of the maximum distance from the plane in the integral element. After that, the point cloud index is constructed according to the integration results, and finally the vectorized point cloud of high-precision map is obtained. The result not only ensures the basic features of the feature, but also ensures the absolute accuracy of the point cloud.