Random sample consensus-based room mapping using light detection and ranging

Abstract

Light detection and ranging (LiDAR) is a high-accuracy data source for geospatial providers that is displayed in two dimensions (2D) or three dimensions (3D). It is used to measure the distances or 2D or 3D maps of the environment. This study examines a random sample consensus (RANSAC)-based room mapping approach utilizing LiDAR. The RANSAC is used to achieve line fitting as a solution to acquire missing or incomplete point cloud data during the process of room scanning. The maximum x-y distance is proposed to achieve a proper model to fix the missing line during the LiDAR scanning process. Data retrieval uses ground-based LiDAR located in the middle of a certain room with the dimension of 5.76×4.95 m2. To explore a room mapping, a 2D LiDAR YDLIDAR G4 with an operating frequency of 7 Hz is used. The derived raw data is then visualized with MATLAB. The results show that the RANSAC can perform line-fitting for missing or illegible LiDAR point cloud data during the scanning process due to reflection or obstacles. The increase in the amount of data used is then directly proportional to the probability of the number of correct models.