Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

This paper describes the full development process of TriMARES, a hybrid AUV/ROV designed to fulfil the requirements of a consortium for the inspection and periodic monitoring of a large dam reservoir. The demand of robotic systems for underwater operations is growing exponentially and there are many scenarios for which the commercial solutions are not adequate. Such was the case with TriMARES, where it was possible to take advantage of previous designs to achieve a custom solution in a short time. We describe the initial requirements for the underwater system, we present the main solutions adopted for the vehicle subsystems, and we provide some data from the first in-water tests, performed only 6 months after the beginning of the project.

BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps.

Traditional coverage path planners create lawnmower-type paths in the operating area completely ignoring the uncertainty in the vehicle's position. However, in the presence of significant uncertainty in localization estimates, one can no longer guarantee that the vehicle will cover all the area according to plan. Aiming to bridge this gap, we present a coverage path planning technique for search operations which takes into account the vehicle's position and detection performance uncertainties and tries to minimize this uncertainty along the planned path. The objective is to plan paths, using a localization error model as input, to reduce as much uncertainty as possible and to minimize the extra path length (swath overlap) while satisfying mission feasibility constraints. We introduce an algorithm that calculates what will be the best moments for bringing the vehicle to surface to ensure a bounded position error. We also consider time and energy constraints that may influence the planned trajectory as path overlap is increased to account for uncertainty. Additionally we challenge the assumption frequently seen in coverage algorithms where two observations of the same target are considered independent.

Several monitoring approaches have been used to understand the physical, chemical, and biological processes associated with coastal sewage discharges. However, these efforts have not improved the understanding of the interaction of effluent plume/coastal ocean processes. Autonomous underwater vehicles (AUVs) have already been shown to be very useful for performing high-resolution surveys of small features such as outfall plumes. Some of the advantages of these platforms include easier field logistics, low cost per deployment, good spatial coverage, sampling over repeated sections, and the ability to perform feature-based or adaptive sampling. Once the data have been collected in the field, it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information; for example, regarding the spatial distribution of soil properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work, geostatistics is used to model and map the spatial distribution of temperature measurements gathered by an AUV in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology can provide good estimates of the dispersion of effluent, and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.