Extraction of planar features from Swissranger SR-3000 Range Images by a clustering method using Normalized Cuts

This paper describes a new approach to extract planar features from 3D range data captured by a range imaging sensor-the SwissRanger SR-3000. The focus of this work is to segment vertical and horizontal planes from range images of indoor environments. The method first enhances a range image by using the surface normal information. It then partitions the Normal Enhanced Range Images (NERI) into a number of segments using the Normalized-Cuts (N-Cuts) algorithm. A least-square plane is fit to each segment and the fitting error is used to determine if the segment is planar or not. From the resulting planar segments, each vertical or horizontal segment is labeled based on the normal of its least-square plane. A pair of vertical or horizontal segments is merged if they are neighbors. Through this region growing process, the vertical and horizontal planes are extracted from the range data. The proposed method has a myriad of applications in navigating mobile robots in indoor environments.