Fast plane extraction in 3D range data based on line segments

This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the point neighborhood structure in data acquired from 3D sensors like range cameras, laser range finders and Microsoft Kinect, it divides the plane-segment extraction task into three steps. The first step is a 2D line segment extraction from raw sensor data, interpreted as 2D data, followed by a line segment based connected component search. The final step finds planes based on connected segment component sets. The first step inspects 2D sub spaces only, leading to a line segment representation of the 3D scan. Connected components of segments represent candidate sets of coplanar segments. Line segment representation and connected components vastly reduce the search space for the plane-extraction step. A region growing algorithm is utilized to find coplanar segments and their optimal (least square error) plane approximation. Region growing contains a fast plane update technique in its core, which combines sets of co-planar segments to form planar elements. Experiments are performed on real world data from different sensors.