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About

About

Aníbal Matos received a PhD in Electrical and Computer Engineering form Porto University in 2001. He is currently associate professor at the Faculty of Engineering of Porto University and a member of the board of directors at INESC TEC. His main research interests are related to perception, sensing, navigation, and control of autonomous marine robots, being the author or co-author of more than 80 publications in international journals and conferences. He has participated and lead several research projects on marine robotics and its application to monitoring, inspection, search and rescue, and defense.

Details

Details

  • Name

    Aníbal Matos
  • Role

    Member of the Board of Directors
  • Since

    01st June 2009
027
Publications

2023

Limit Characterization for Visual Place Recognition in Underwater Scenes

Authors
Gaspar, AR; Nunes, A; Matos, A;

Publication
ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

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.

2023

Labelled Indoor Point Cloud Dataset for BIM Related Applications

Authors
Abreu, N; Souza, R; Pinto, A; Matos, A; Pires, M;

Publication
DATA

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).

2023

Construction progress monitoring - A virtual reality based platform

Authors
Abreu, N; Pinto, A; Matos, A; Pires, M;

Publication
Iberian Conference on Information Systems and Technologies, CISTI

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.

2023

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review

Authors
Abreu, N; Pinto, A; Matos, A; Pires, M;

Publication
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION

Abstract
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.

2023

Comparative Study of Semantic Segmentation Methods in Harbour Infrastructures

Authors
Nunes, A; Gaspar, AR; Matos, A;

Publication
OCEANS 2023 - LIMERICK

Abstract
Nowadays, the semantic segmentation of the images of the underwater world is crucial, as these results can be used in various applications such as manipulation or one of the most important in the semantic mapping of the environment. In this way, the structure of the scene observed by the robot can be recovered, and at the same time, the robot can identify the class of objects seen and choose the next action during the mission. However, semantic segmentation using cameras in underwater environments is a non-trivial task, as it depends on the quality of the acquired images (which change over time due to various factors), the diversification of objects and structures that can be inspected during the mission, and the quality of the training performed prior to the evaluation, as poor training means an incorrect estimation of the object class or a poor delineation of the object. Therefore, in this paper, a comparative study of suitable modern semantic segmentation algorithms is conducted to determine whether they can be used in underwater scenarios. Nowadays, it is very important to equip the robot with the ability to inspect port facilities, as this scenario is of particular interest due to the large variety of objects and artificial structures, and to know and recognise most of them. For this purpose, the most suitable dataset available online was selected, which is the closest to the intended context. Therefore, several parameters and different conditions were considered to perform a complete evaluation, and some limitations and improvements are described. The SegNet model shows the best overall accuracy, reaching more than 80%, but some classes such as robots and plants degrade the quality of the performance (considering the mean accuracy and the mean IoU metric).

Supervised
thesis

2022

Radar based static 3D occupancy map

Author
Gaspar Santos Pinheiro

Institution
UP-FEUP

2022

Underwater 3D reconstruction and reinforcement of machine learning in object recognition

Author
Alexandra Pereira Nunes

Institution
UP-FEUP

2022

Adaptive close-range navigation for inspection pf underwater structures

Author
Ana Rita da Silva Gaspar

Institution
UP-FEUP

2022

Out of Distribution Detection in Camera Perception for Autonomous Driving

Author
Gustavo Manuel de Oliveira Bastos Cordeiro

Institution
UP-FEUP

2022

Distributed Perception from Multiple Intelligent Systems for Offshore Marine Surveys

Author
Daniel Filipe Barros Campos

Institution
UP-FEUP