Abstract
Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.
Highlights
The evolution of sensor technology in recent decades has made possible the acquisition of 3D data in an accurate and quick way
This paper presents a method to optimize the number and position of scans for Scan-vs-Building Information Models (BIM) procedures following stop & go scanning method, and the shortest route for an autonomous robot visiting once all the optimal scan positions
The input for the method is a DXF file exported by BIM in which elements are organized by layers
Summary
The evolution of sensor technology in recent decades has made possible the acquisition of 3D data in an accurate and quick way. This has stimulated interest in its use in different fields, especially in Architecture, Engineering and Construction (AEC). Within these disciplines, Image-Based and Time-of-Flight-Based technologies have been the two major technologies used to acquire spatial data [1]. Light Detection and Ranging (LiDAR) sensors such as both Terrestrial and Mobile Laser Scanners (TLS and MLS) provide highly accurate geometric data in point cloud format. Increasing 3D spatial acquisitions with LiDAR devices has raised an interest in automated processing of point clouds in researchers of remote sensing, computer vision and robotic communities
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