With dead-reckoning from velocity sensors, AUVs may construct short-term, local bathymetry maps of the sea floor using multibeam sensors. However, the position estimate from dead-reckoning will include some drift that grows with time. In this work, we focus on long-term onboard storage of these local bathymetry maps, and the alignment of maps with respect to each other. We propose using Sparse Gaussian Processes for this purpose, and show that the representation has several advantages, including an intuitive alignment optimization, data compression, and sensor noise filtering. We demonstrate these three key capabilities on two real-world datasets. ; QC 20191024
Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry
2018-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Autonomous vehicles , Bathymetry , Digital storage , Gaussian distribution , Gaussian noise (electronic) , Navigation , Alignment optimization , Dead reckoning , Multi-beam sensors , Multibeam bathymetry , Position estimates , Real-world datasets , Sparse Gaussian process , Velocity sensor , Autonomous underwater vehicles , Vehicle Engineering , Farkostteknik , Computer Vision and Robotics (Autonomous Systems) , Datorseende och robotik (autonoma system)
DDC: | 629 |
British Library Conference Proceedings | 2013
|Beyond Bathymetry: Water Column Imaging with Multibeam Echo Sounder Systems
HENRY – Bundesanstalt für Wasserbau (BAW) | 2014
|Comparison of laser bathymetry with single-beam and multibeam data
HENRY – Bundesanstalt für Wasserbau (BAW) | 2015
|British Library Conference Proceedings | 2005
|3D SLAM Method Based on Improved Regionalized Gaussian Process Map Construction
Springer Verlag | 2021
|