Robust Segmentation of Building Planar Features From Unorganized Point Cloud

Abstract

This paper presents a novel method for segmentation of planar feature from unorganized point cloud based on 2D Hough Transform and octree. Given the input point cloud, three steps are performed to segment planar features. Firstly, the original point cloud is sampled and projected to the X-Y plane, and an extended 2D Hough transform algorithm is employed to extract the line segments. The selecting weight iteration method is used to calculate line equations and endpoint coordinates of those line segments. The space geometric equations of the vertical planes are then determined. Secondly, the octree structure of the original point cloud is established, and then the exact endpoint coordinates of the line segment are used to design a cube perpendicular to the X-Y plane and all points held by the cube are extracted. The distance from each of the extracted points inside the cube to its corresponding facade is calculated, if the distance is less than the predefined threshold, the point is regarded as a point inside the facade. Finally, all the facade points are removed from the original point cloud, and remaining point cloud is sampled and projected to the X-Z plane. The above process is repeated to extract horizontal planes. Evaluation experiments are performed by analyzing the performance of our method in four different scenes. The experimental results indicate that the proposed algorithm is suitable for segmentation of building planar features in different scenes. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones.

Keywords

• Hough transform
• unorganized point clouds
• planar feature
• selecting weight iteration
• octree