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Ignacio Torroba

7 accepted papers

2025

Efficient Non-Myopic Layered Bayesian Optimization for Large-Scale Bathymetric Informative Path Planning

ICRA 2025

Informative path planning (IPP) applied to bathy-metric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are sho

Cited by 2SourcecodeScholar
2025

Side Scan Sonar-based SLAM for Autonomous Algae Farm Monitoring

IROS 2025

The transition of seaweed farming to an alternative food source on an industrial scale relies on automating its processes through smart farming, equivalent to land agriculture. Key to this process are autonomous underwater vehicles (AUVs) via their capacity to automate crop and structural inspection

Cited by 0SourcecodeScholar
2023

Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM

ICRA 2023poster

Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is part…

Cited by 12SourcecodeScholar
2023

Online Stochastic Variational Gaussian Process Mapping for Large-Scale Bathymetric SLAM in Real Time

RA-L 2023

Rao-Blackwellized particle filter (RBPF) SLAM solutions with Gaussian Process (GP) maps can both maintain multiple hypotheses of a vehicle pose estimate and perform implicit data association for loop closure detection in continuous terrain representations. Both qualities are of particular interest f

Cited by 15SourceScholar
2022

Fully-Probabilistic Terrain Modelling and Localization With Stochastic Variational Gaussian Process Maps

RA-L 2022

Gaussian processes (GPs) are becoming a standard tool to build terrain representations thanks to their capacity to model map uncertainty. This effectively yields a reliability measure of the areas of the map, which can be directly utilized by Bayes filtering algorithms in robot localization problems

Cited by 15SourceScholar
2020

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

RA-L 2020

Registration methods for point clouds have become a key component of many SLAM systems on autonomous vehicles. However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter. This estimate, which is norm

Cited by 21SourceScholar