Abstract
For Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAVs) to operate autonomously and effectively, it is mandatory to endow them with precise landing abilities. The UAV has to be able to detect the landing target and to perform the landing maneuver without compromising its own safety and the integrity of its surroundings. However, current UAVs do not present the required robustness and reliability for precise landing in highly demanding scenarios, particularly due to their inadequacy to perform accordingly under challenging lighting and weather conditions, including in day and night operations.This work proposes a multimodal fiducial marker, named ArTuga (Augmented Reality Tag for Unmanned vision-Guided Aircraft), capable of being detected by an heterogeneous perception system for accurate and precise landing in challenging environments and daylight conditions. This research combines photometric and radiometric information by proposing a real-time multimodal fusion technique that ensures a robust and reliable detection of the landing target in severe environments.Experimental results using a real multicopter UAV show that the system was able to detect the proposed marker in adverse conditions (such as at different heights, with intense sunlight and in dark environments). The obtained average accuracy for position estimation at 1 m height was of 0.0060 m with a standard deviation of 0.0003 m. Precise landing tests obtained an average deviation of 0.027 m from the proposed marker, with a standard deviation of 0.026 m. These results demonstrate the relevance of the proposed system for the precise landing in adverse conditions, such as in day and night operations with harsh weather conditions.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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).

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