A METHOD FOR DETECTION OF DOORS IN BUILDING INDOOR POINT CLOUD THROUGH MULTI-LAYER THRESHOLDING AND HISTOGRAM ANALYSIS

Abstract

Indoor navigation is a critical service providing safe paths for humans in an emergency. Since doors connect different parts of a building, door detection is essential in creating a navigation map and walkable spaces. Considering the Manhattan World Assumption (MWA), this paper proposes a method for detecting doors that connect two scanned rooms. In contrast with most existing approaches, the proposed method requires neither trajectory nor scanning position. This method consists of two main steps. At first, with the help of multi-layer thresholding, a raster will be created from the point cloud that its Digital Numbers (DNs) correspond to the ceiling elevation. Then, this raster's pixels will be segmented based on their DNs, and those segments whose elevations are local minimums are chosen as door candidates. The second step extracts the part of the point cloud corresponding to each door candidate and analyses its coordinates components' histograms to decide whether there is a door or not. The proposed scheme has been tested on two different datasets and could accurately detect 91% of the inner doors. Although this method is designed to detect inner doors, it also detected 65% of marginal doors.