Fast planar clustering and polygon extraction from noisy range images acquired in indoor environments

This paper presents a novel algorithm to cluster planar points and extract polygons from 3D range images acquired in an indoor environment. The algorithm replaces large number of data points in the range image with polygons that fits the planar regions of the indoor environment resulting in high data compression. The 3D range image is acquired by panning a laser scanner and the data is stored in a 2D array. The elements in the array are stored as spherical coordinates and the indices of the array retain neighborhood information. The array is segmented into small patches and Hessian plane parameters are computed for each planar patch. We propose a Breadth First Search (BFS) graph-search algorithm to compare the plane parameters of neighboring patches and cluster the coplanar patches into respective planes. Experimental result shows 94.67% average compression rate for indoor scans. In addition, the algorithm shows a vast improvement in speed when compared to an improvised region-growing algorithm that extracts polygons from range images.