In this paper, the author proposed a segmentation method based on the fundamental geographic data, the algorithm describes as following: Firstly, convert the coordinate system of fundamental geographic data to that of vehicle- borne LiDAR point cloud though some data preprocessing work, and realize the coordinate system between them; Secondly, simplify the feature of fundamental geographic data, extract effective contour information of the buildings, then set a suitable buffer threshold value for building contour, and segment out point cloud data of building facades automatically; Thirdly, take a reasonable quality assessment mechanism, check and evaluate of the segmentation results, control the quality of segmentation result. Experiment shows that the proposed method is simple and effective. The method also has reference value for the automatic segmentation for surface features of other types of point cloud.
A rod-like objects extraction method based on clustering is presented. Firstly, project the original point clouds onto the horizontal plane, divide them into grids, and take a single grid as data processing unit to remove the ground points; Secondly, make the grid based on processing data for point clouds detection and numbered, give the same attribute values and cluster the object points using eight neighborhood search method. Then, take the clustered single point clouds as processing units, take advantage of various object features, such as height, density projection, the projection area and shape to exclude the other object points progressively, and achieve the fine extraction of the rod-like objects. The experiment tests the validity of the method described in the text of the extraction of the rod-like objects in road environment.