Open AccessRepresentative Construction Engineering Drawings Combining SLAM and Ground-Based LiDAR
Author(s) -
Chutian Gao,
Minghui Guo,
Zexin Fu,
Dengke Li,
Xianyue Ren,
Mengxi Sun,
Yuquan Zhou,
Peng Cheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2112/1/012017
Subject(s) - point cloud , lidar , laser scanning , computer science , elevation (ballistics) , preprocessor , data pre processing , simultaneous localization and mapping , remote sensing , ranging , building model , artificial intelligence , computer vision , simulation , engineering , robot , geography , laser , telecommunications , physics , structural engineering , optics , mobile robot
Obtaining architectural engineering drawings is a crucial aspect of upgrading and repairing structures. Traditional elevation measuring is ineffective and results in a poor rate of restoration. The current building elevation measurement solutions based on 3D scanning technology all obtain building 3D point cloud data from a single type of laser scanner. These two methods can’t get both indoor and outdoor scenes at the same time. This paper presents a scanning strategy that combines SLAM with Ground-based LiDAR to solve this problem. The point cloud data for the building’s indoor and outdoor scenes are obtained independently, and the Ground-based LiDAR point cloud data is registered locally using the iterative closest point(ICP) algorithm. The SLAM point clouds and the Ground-based LiDAR point clouds are then registered as a whole to develop an overall model of the building using point constrained error equations. For various reasons, the building can be trimmed into a planar point cloud model depending on the application. Finally, engineering drawings for the construction of the building can be drawn. The method’s viability was demonstrated by using it in a 3D scanning project of a scenic site in Beijing. This technology improves model information interpretability, scanning efficiency, and provides powerful data assistance for building rehabilitation and repair. It is extremely important in the disciplines of urban planning, rehabilitation, and historic preservation. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.