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Building information modelling (BIM) is evolving significantly in the architecture, engineering and construction industries. BIM involves various remote-sensing tools, procedures and standards that are useful for collating the semantic information required to produce 3D models. This is thanks to LiDAR technology, which has become one of the key elements in BIM, useful to capture a semantically rich geometric representation of 3D models in terms of 3D point clouds. This review paper explains the 'Scan to BIM' methodology in detail. The paper starts by summarising the 3D point clouds of LiDAR and photogrammetry. LiDAR systems based on different platforms, such as mobile, terrestrial, spaceborne and airborne, are outlined and compared. In addition, the importance of integrating multisource data is briefly discussed. Various methodologies involved in point-cloud processing such as sampling, registration and semantic segmentation are explained in detail. Furthermore, different open BIM standards are summarised and compared. Finally, current limitations and future directions are highlighted to provide useful solutions for efficient BIM models. [ABSTRACT FROM AUTHOR]

An inappropriately maintained road cut-slope is likely to fail, resulting in landslides or falling rocks that compromise road safety. Thus, road managers need to know the location of dangerous slopes along the road in order to prevent these events from happening. In this article, we compare two different approaches for conducting the digitization of the road environment and the automatic detection and delimitation of road slopes: Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS). The point clouds obtained using the first kind of devices are dense, rich in detail and generated from a ground perspective; the second type of scanners produce less dense clouds from a zenithal perspective. We explore what is the effect of the point cloud density and scanner point of view over the slope detection procedure. Two road segments from the Spanish A55 and A52 highways were used as study zones, and a total of 28.61 km were analyzed. Better detection and delimitation results were achieved when using the ALS data and its corresponding algorithm. It was observed that the higher point density and detail of the MLS clouds were not an advantage for the slope detection task, and that measuring the road from a terrestrial perspective affected in a negative way during the detection process: the crest of the slopes often remained unmeasured, hidden behind vegetation or man-made elements, thus resulting in the slopes not being complete in the MLS clouds. Meanwhile, the whole slope structure is scanned when the scene is measured from an aerial perspective, henceforth obtaining better detection rates despite the relatively low resolution. The findings of this study provide valuable information in the field of road asset management, and help road managers make decisions when choosing what technology to use for the data gathering process. • Different laser scanning systems were compared for measuring the road environment. • Automatic detection of critical road slopes was achieved using point clouds. • More slopes were detected using low density aerial point clouds. • Importance of the point of view and point density of the cloud are discussed. [ABSTRACT FROM AUTHOR]

Individual tree (IT) segmentation is crucial for forest management, supporting forest inventory, biomass monitoring or tree competition analysis. Light detection and ranging (LiDAR) is a prominent technology in this context, outperforming competing technologies. Aerial laser scanning (ALS) is frequently used for forest documentation, showing good point densities at the tree-top surface. Even though under-canopy data collection is possible with multi-echo ALS, the number of points for regions near the ground in leafy forests drops drastically, and, as a result, terrestrial laser scanners (TLS) may be required to obtain reliable information about tree trunks or under-growth features. In this work, an IT extraction method for terrestrial backpack LiDAR data is presented. The method is based on DBSCAN clustering and cylinder voxelization of the volume, showing a high detection rate (∼90%) for tree locations obtained from point clouds, and low commission and submission errors (accuracy over 93 % ). The method includes a sensibility assessment to calculate the optimal input parameters and adapt the workflow to real-world data. This approach shows that forest management can benefit from IT segmentation, using a handheld TLS to improve data collection productivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost. [ABSTRACT FROM AUTHOR]

Construction quality and progress control are demanding, yet critical construction activities. Building Information Models and as-built scanned data can be used in Scan-vs-BIM processes to effectively and comprehensively support these activities. This however requires accurate registration of scanned point clouds with 3D (BIM) models. Automating such registration remains a challenge in the context of the built environment, because as-built can be incomplete and/or contain data from non-model objects, and construction buildings and other structures often present symmetries and self-similarities that are very challenging to registration. In this paper, we present a novel automatic coarse registration method that is an adaptation of the ‘4 Points Congruent Set’ algorithm to the use of planes; we call it the ‘4-Plane Congruent Set’ (4-PlCS) algorithm. The approach is further integrated in a software system that delivers not one but a ranked list of the most likely transformations, so to allow the user to quickly select the correct transformation, if need be. Two variants of the method are also considered, in particular one in the case when the vertical axis is known a priori; we call that method the 4.5-PlCS method. The proposed algorithm is tested using five different datasets, including three simulated and two real-life ones. The results show the effectiveness of the proposed method, where the correct transformation always ranks very high (in our experiments, first or second), and is extremely close to the ground-truth transformation. Experimental comparison of the proposed approach with a standard, more intuitive approach based on finding 3-plane congruent sets shows the discriminatory power of 4-plane bases over 3-plane bases, albeit at no clear benefits in terms of computational time. The experimental results for the 4.5-PlCS method show that it delivers a non-negligible reduction in computational time (approx. 20%), but at no additional benefit in terms of effectiveness in finding the correct transformation. [ABSTRACT FROM AUTHOR]