Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry

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 loca...

Full description

Saved in:
Bibliographic Details
Main Authors: Bore, Nils, Torroba, Ignacio, Folkesson, John
Format: Conference Proceeding
Language:English
Subjects:
Dead reckoning
Gaussian processes
Optimization
Sea measurements
Simultaneous localization and mapping
Three-dimensional displays
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2377-6536
DOI:10.1109/AUV.2018.8729748