We propose a low-cost laboratory platform for development and validation of underwater mapping techniques, using the BlueROV2 Remotely Operated Vehicle (ROV). Both the ROV and the objects to be mapped are placed in a pool that is imaged via an overhead camera. In our prototype mapping application, the ROV's pose is found using extended Kalman filtering on measurements from the overhead camera, inertial, and pressure sensors; while objects are detected with a deep neural network in the ROV camera stream. Validation experiments are performed for pose estimation, detection, and mapping. The litter detection dataset and code are made publicly available.
@article{arxiv.2407.10901,
title = {A BlueROV2-based platform for underwater mapping experiments},
author = {Tudor Alinei-Poiana and David Rete and Davian Martinovici and Vicu-Mihalis Maer and Lucian Busoniu},
journal= {arXiv preprint arXiv:2407.10901},
year = {2024}
}
Comments
Accepted at 15th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles, Blacksburg, Virginia, 3-5 September 2024