Building Floor Plan Reconstruction From Slam-Based Point Cloud Using Ransac Algorithm

Abstract

Abstract. In recent years, the applications of interior and exterior model of buildings have been increased in the field of surveying and mapping. This paper presents a new method for extracting a two-dimensional (2D) floor plan of a building from Simultaneous localization and mapping (SLAM)-based point clouds. In the proposed algorithm, after preprocessing, the voxel space is generated for the point cloud. Then, the optimal section of the voxel cube to generate building floor plan is identified. Finally, the linear structures and walls are extracted using the random sample consensus (RANSAC) algorithm. The proposed algorithm was examined on a collected point clouds of a building, and the walls of this building were automatically extracted. To evaluate the proposed method, the obtained walls by the algorithm were compared with the manually extracted walls. The algorithm successfully extracted almost 90% of the walls in the test area. Moreover, the average error of 3 cm for the extracted walls proved the high accuracy of the proposed method for building floor plan modeling.

Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach

“…The 2D line derived from the structural elements was used in the as-built BIM. Pouraghdam et al [15] selected the horizontal slice intended for RANSAC applications 0.3 m below the ceiling. Gankhuyag and Han [14] determined the z-coordinate of the floor and the ceiling to determine indoor height.…”

“…The most typical method for removing indoor clutter objects is the line-fitting-based method. The line-fitting-based approach identifies outliers using obtained lines or planes that represent the additional elements that need to be preserved [9,10,[14][15][16]. However, the line-fitting-based method often ignores certain types of elements, such as indoor columns or walls, depending on their parameter values defined a priori, and it may not accurately reflect the thickness of inner walls [14,16].…”

A Semi-Automated Approach to Model Architectural Elements in Scan-to-Bim Processes

“…• parametric modelling (e.g., model matching or fitting): it uses standard mathematical expressions, such as cubes, planes and cylinders (Kyriakaki-Grammatikaki et al, 2022) in order to fit shapes into clusters (model fitting method). The estimation of these shapes allows the user to recognise the main architectural elements of an existing building, such as floors and ceilings (Leoni et al, 2019), wall surfaces (Hossein Pouraghdam et al, 2019), and primitive geometriesmasses -describing indoor spaces (Jenke et al, 2009). Representative algorithms are HT algorithm (Hough-Transform), sample consensus (RANSAC) (Schnabel et al, 2007) and maximum likelihood estimation SAC (e.g., MLESACT).…”

Parametric Modelling Approach to Reconstructing Architectural Indoor Spaces From Point Clouds

“…Many investigations are based on the use of RANSAC algorithm (Random sample consensus) (Fischler and Bolles, 1981) to estimate planes in dense point clouds. The estimation of planes is a very convenient step for recognising main building elements such as floors, ceilings (Leoni et al, 2019), wall surfaces (Hossein Pouraghdam et al, 2019) or for the detection of cuboids which represent interior spaces (Jenke et al, 2009). Another common method of recognising planes and orientations is to use histogram analysis according to a specific direction (Okorn et al, 2010).…”