An extended normalized cuts method for real-time planar feature extraction from noisy range images

This paper presents a new method for extracting planar features from noisy range data. The method encodes the local geometric information (surface normals) and global spatial information (coordinates) of 3D data points into an Enhanced Range Image (ERI) which is then clustered into a number of homogeneous groups, called Super Pixels (SPs). The Normalized Cuts (NC) method is employed to the graph built on the SPs and groups the SPs into planar segments. The ERI coding enhances object surfaces and edges while its sensitivity to surface normals is suppressed by the NC measure that takes into account the spatial information of SPs in computing the edge weights of the graph. A binary matrix is constructed to represent the spatial and similarity relationships among the planar segments. We then employ a search algorithm on this matrix to merge homogenous planar segments. The proposed approach is compared with a representative plane segmentation method in various indoor environments and the results demonstrate the efficacy of the proposed method. In this paper, rang data are captured from a 3D imaging sensor-the Swissranger SR-3000.