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.

<p><strong>Abstract.</strong> Scan-to-BIM systems have been recently proposed for the dimensional and quality assessment of as-built construction components with planned works. The procedure is generally based on the geometric alignment and comparison of as-built laser scans with as-designed BIM models. A major concern in Scan-to-BIM procedures is point cloud quality in terms of data completeness and consequently, the scanning process should be designed in order to obtain a full coverage of the scene while avoiding major occlusions. This work proposes a method to optimize the number and scan positions for Scan-to-BIM procedures following stop &amp;amp; go scanning. The method is based on a visibility analysis using a <i>ray-tracing algorithm</i>. In addition, the optimal route between scan positions is formulated as a <i>travelling salesman problem</i> and solved using a suboptimal <i>ant colony optimization algorithm</i>. The distribution of candidate positions follows a grid-based structure, although other distributions based on triangulation or tessellation can be implemented to reduce the number of candidate positions and processing time.</p>

Abstract. Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

This paper proposes an efficient and simplified procedure for the 3D modelling of buildings, based on the semi-automatic processing of point clouds acquired with mobile LiDAR scanners. The procedure is designed with the aim at generating BIM, in gbXML format, from the point clouds. In this way, the main application of the procedure is the performance of energy analysis, towards the increase of the energy efficiency in the construction sector, and its consequent contribution to the mitigation of the climate change. Thus, the main contribution of the methodology proposed is its easiness of use and its level of automation, which allow its utilization by users who are experts in the use of energy in buildings but non-experts on 3D modelling. The software provides a solution for the 3D modelling of complex point clouds of various millions of points in times of execution less than 10 minutes. The system is evaluated through its application to three different real-world scenarios and compared with manual modelling. Moreover, the results are used for an example of an energy application, proving their performance against manually elaborated models.