Parametric Classification of Furniture From Point Cloud Developed Using Low Cost Trolley Based Laser Scanning System

Abstract

This paper presents a parametric classification methodology to identify common indoor and outdoor furniture objects present in the 3D Cartesian point cloud of the surveyed environment. For this purpose, a low cost custom made trolley based scanning and surveying system has developed using orthogonal integration of two popular Hokuyo-30LX 2D laser scanners. The developed system has been successfully used to generate 3D point cloud of the environment using Simultaneous Localization and Mapping (SLAM) technique. The instrumentation system of the trolley has been interfaced through Robot Operating System (ROS) for online processing and recording of all sensorial data. While classification of the furniture present in point cloud has been done in offline mode using Random Sampling and Consensus (RANSAC) based parametric segmentation technique. The innovative furniture detection has applied on each scan in order to reduce the region of interest in the developed point cloud. In addition, the validation of the classified furniture objects has been performed using Fuzzy Logic. Multiple indoor and outdoor vicinities have been scanned and modelling results have been found accurate nearer to ground truth. In comparison to available surveying solutions present in the local market, the developed system has been found faster and precise to produce more enhanced structural results with minute details.