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030
Publications

2022

3DupIC: An Underwater Scan Matching Method for Three-Dimensional Sonar Registration

Authors
Ferreira, A; Almeida, J; Martins, A; Matos, A; Silva, E;

Publication
SENSORS

Abstract
This work presents a six degrees of freedom probabilistic scan matching method for registration of 3D underwater sonar scans. Unlike previous works, where local submaps are built to overcome measurement sparsity, our solution develops scan matching directly from the raw sonar data. Our method, based on the probabilistic Iterative Correspondence (pIC), takes measurement uncertainty into consideration while developing the registration procedure. A new probabilistic sensor model was developed to compute the uncertainty of each scan measurement individually. Initial displacement guesses are obtained from a probabilistic dead reckoning approach, also detailed in this document. Experiments, based on real data, demonstrate superior robustness and accuracy of our method with respect to the popular ICP algorithm. An improved trajectory is obtained by integration of scan matching updates in the localization data fusion algorithm, resulting in a substantial reduction of the original dead reckoning drift.

2021

Autonomous High-Resolution Image Acquisition System for Plankton

Authors
Resende, J; Barbosa, P; Almeida, J; Martins, A;

Publication
2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract

2021

Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

Authors
Loureiro, G; Dias, A; Martins, A; Almeida, J;

Publication
REMOTE SENSING

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

2021

LiDAR-based Power Assets Extraction based on Point Cloud Data

Authors
Amado, M; Lopes, F; Dias, A; Martins, A;

Publication
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021, Santa Maria da Feira, Portugal, April 28-29, 2021

Abstract

2021

Graph-SLAM Approach for Indoor UAV Localization in Warehouse Logistics Applications

Authors
Moura, A; Antunes, J; Dias, A; Martins, A; Almeida, J;

Publication
2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
Unmanned Aerial Vehicles (UAVs) are a key ingredient in the industry and in warehouse logistics digital transformation process, providing the ability to perform automatic cyclic counting and real-time inventory, localize hard-to-find items and reach narrow storage areas. The use of UAVs poses new challenges, such as indoor autonomous localization and navigation, collision avoidance and automated UAV fleet management. This paper addresses the development of a vision-based Graph-SLAM approach for UAV indoor localization without predefined warehouse markers positions. A framework is proposed and developed to support different commercial UAV platforms, allowing the estimation in real-time of the UAV position and attitude. Indoor experimental tests were carried out in order to evaluate the performance of the developed method, comparing the results obtained with an approach based on the pre-mapped markers position indoor localization method.

Supervised
thesis

2020

Sistema Autónomo de Aquisição de Imagens de Alta Resolução de Plâncton

Author
JOÃO FILIPE AMORIM RESENDE

Institution
IPP-ISEP

2020

Interface Homem-Máquina Multi Robótica em Unity3D

Author
RUI RODRIGO SERRA FIGUEIRINHA

Institution
IPP-ISEP

2020

Sistema Autónomo de Recolha de Informação Genética para Meio Aquático

Author
PEDRO EMANUEL JORGE BARBOSA

Institution
IPP-ISEP

2019

Sistema de Posicionamento Acústico e Transmissão de Dados para Alvos Subaquáticos

Author
NUNO MANUEL COUTO VIANA

Institution
IPP-ISEP

2019

Bearing Based Low Cost Underwater Acoustic Positioning System

Author
PEDRO EMANUEL DE ALVES GUEDES

Institution
IPP-ISEP