Multi-Sensor Mapping for Low Contrast, Quasi-Dynamic, Large Objects
Vikrant Shah, Kristin Schild, Margaret Lindeman, Daniel Duncan, David Sutherland, Claudia Cenedese, Fiammetta Straneo, Hanumant Singh
Abstract
This paper proposes a systems level solution for addressing the problem of mapping large moving targets with slow but complicated dynamics. Our approach utilizes the complementary nature of multiple sensing modalities. While this work is applicable to other domains we focus our efforts on mapping rotating and translating icebergs. Our solution involves a rigidly coupled combination of a line scan sensor - a subsurface multibeam sonar, with an area scan sensor - an optical camera. This allows the system to exploit the optical camera information to perform iceberg relative navigation which can directly be used by the multibeam sonar to map the iceberg underwater. This paper details the algorithm required to compute the scale of the navigation solution and corrections to find iceberg centric navigation and thus an accurate iceberg reconstruction. This approach is successfully demonstrated on real world iceberg data collected during the 2018 Sermilik campaign in Eastern Greenland. Due to the availability of iceberg mounted GPS observations during this research expedition we could also groundtruth our navigation and thus our systems level mapping efforts.
BibTeX
@inproceedings{ral2020_multisensormappi,
title = {Multi-Sensor Mapping for Low Contrast, Quasi-Dynamic, Large Objects},
author = {Vikrant Shah and Kristin Schild and Margaret Lindeman and Daniel Duncan and David Sutherland and Claudia Cenedese and Fiammetta Straneo and Hanumant Singh},
booktitle = {RA-L 2020},
year = {2020}
}