Abstract
Depth control is crucial for underwater vehicles, not only to perform certain tasks that require the vehicle to be still at a given depth but also because most propeller-driven vehicles waste a considerable amount of energy to counteract the passively tuned positive buoyancy. The use of a variable buoyancy system (VBS) can effectively address these items, increasing the energetic efficiency and thus mission length. Achieving accurate depth controllers is, however, a complex task, since experimental controller development in sea or even in test pools is unpractical and the use of simulation requires accurate vertical motion models whose parameters might be difficult to obtain or measure. The development of simple, yet comprehensive, dynamic models for devices incorporating VBS is therefore of upmost importance, as well as developing procedures that allow a simple determination of their parameters. This work contributes to this field by deriving a unified model for the vertical motion of a VBS actuated device, irrespective of the specific technological actuation solution employed, whether it be electromechanical or electrohydraulic. A concise analysis of the open-loop stability of the unified model is presented and a straightforward yet efficient procedure for identifying several of its parameters is introduced. This identification procedure is designed to be convenient and can be carried out in shallow waters, such as test pools, while its results are applicable to the deeper water model as well. To validate the procedure, experimental values obtained from an electromechanical VBS actuated device are used. Closed-loop control of the electromechanical VBS actuated device is conducted through simulation and experimental tests. The results confirm the effectiveness of the proposed unified model and the parameter identification methodology.

Abstract
A kernel regressor to estimate a six-degree-of-fredoom non linear model of an autonomous underwater vehicle is proposed. Although this estimator assumes that the model coefficients are linear combinations of basis functions, it circumvents the problem of specifying the basis functions by using the kernel trick. The Gaussian radial basis function is the chosen kernel, with the Kernel matrix being regularized by its principal components. The variance of the Gaussian radial basis function and the number of principal components are hyper-parameters to be determined by the minimisation of a final prediction error criterion and using the training data. A simulated autonomous underwater vehicle is proposed was used as case study.

Abstract
Blob features are particularly common in acoustic imagery, as isolated objects (e.g., moorings, mines, rocks) appear as blobs in the acquired images. This work focuses the application of the SIFT, SURF, KAZE and U-SURF feature extraction algorithms for blob feature tracking towards Simultaneous Localization and Mapping applications. We introduce a modified feature extraction and matching pipeline intended to improve feature detection and matching precision, tackling performance deterioration caused by the differences between optical and acoustic imagery. Experimental evaluation was undertaken resorting to datasets collected from a water tank structure.

Abstract
The underwater environment has some structures that still need regular inspection. However, the nature of this environment presents a number of challenges in achieving accurate vehicle position and consequently successful image similarity detection. Although there are some factors - water turbidity or light attenuation - that degrade the quality of the captured images, visual sensors have shown a strong impact on mission scenarios - close range operations. Therefore, the purpose of this paper is to study whether these data are capable of addressing the aforementioned underwater challenges on their own. Considering the lack of available data in this context, a typical underwater scenario was recreated using the Stonefish simulator. Experiments were conducted on two predefined trajectories containing appearance scene changes. The loop closure situations provided by the bag-of-words (BoW) approach are correctly detected, but it is sensitive to some severe conditions.

Abstract
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. Dataset: https://doi.org/10.5281/zenodo.7948116 Dataset License: Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract
Precise construction progress monitoring has been shown to be an essential step towards the successful management of a building project. However, the methods for automated construction progress monitoring proposed in previous work have certain limitations because of inefficient and unrobust point cloud processing. The main objective of this research was to develop an accurate automated method for construction progress monitoring using a 4D BIM together with a 3D point cloud obtained using a terrestrial laser scanner. The proposed method consists of four phases: point cloud simplification, alignment of the as-built data with the as-planned model, classification of the as-built data according to the BIM elements, and estimation of the progress. The accuracy and robustness of the proposed methodology was validated using a known dataset. The developed application can be used for construction progress visualization and analysis. © 2023 ITMA.