1 citations · 3 across the 6 of their papers we have counts for
7 papers
Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
Michal Tešnar, Bilal Wehbe, Matias Valdenegro-Toro
Machine learning proves effective in constructing dynamics models from data, especially for underwater vehicles. Continuous refinement of these models using incoming data streams,…
The Marine Debris Forward-Looking Sonar Datasets
Matias Valdenegro-Toro, Deepan Chakravarthi Padmanabhan, Deepak Singh +2
Sonar sensing is fundamental for underwater robotics, but limited by capabilities of AI systems, which need large training datasets. Public data in sonar modalities is lacking. Thi…
Self-supervised Learning for Sonar Image Classification
Alan Preciado-Grijalva, Bilal Wehbe, Miguel Bande Firvida +1
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great int…
The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation
Deepak Singh, Matias Valdenegro-Toro
Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected u…
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study
Mihir Patil, Bilal Wehbe, Matias Valdenegro-Toro
Docking control of an autonomous underwater vehicle (AUV) is a task that is integral to achieving persistent long term autonomy. This work explores the application of state-of-the-…
Pre-trained Models for Sonar Images
Matias Valdenegro-Toro, Alan Preciado-Grijalva, Bilal Wehbe
Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the computer vision field due to the lack of data and pre-trained models specificall…