Efficient AUV Navigation Fusing Acoustic Ranging and Side-scan Sonar
Name
Leonard_Efficient AUV.pdf
Size
1 MB
Format
Adobe PDF
Checksum (MD5)
849c0b12c53d1ba22b1bd565ce9f317b
Author(s)
Fallon, Maurice Francis
Kaess, Michael
Johannsson, Hordur
Leonard, John Joseph
Date Issued
May 2011
Journal
2011 IEEE International Conference on Robotics and Automation. Proceedings (ICRA)
Publisher
IEEE Computer Society
Citation
Fallon, Maurice F. et al. "Efficient AUV Navigation Fusing Acoustic Ranging and Side-scan Sonar" IEEE International Conference on Robotics and Automation. Proceedings, (ICRA), May 9-13, 2011,Shanghai International Conference Center, Shanghai, China.
Version
Author's final manuscript
Abstract
This paper presents an on-line nonlinear least
squares algorithm for multi-sensor autonomous underwater
vehicle (AUV) navigation. The approach integrates the global
constraints of range to and GPS position of a surface vehicle
or buoy communicated via acoustic modems and relative pose
constraints arising from targets detected in side-scan sonar images.
The approach utilizes an efficient optimization algorithm,
iSAM, which allows for consistent on-line estimation of the
entire set of trajectory constraints. The optimized trajectory
can then be used to more accurately navigate the AUV, to
extend mission duration, and to avoid GPS surfacing. As iSAM
provides efficient access to the marginal covariances of previously
observed features, automatic data association is greatly
simplified — particularly in sparse marine environments. A
key feature of our approach is its intended scalability to
single surface sensor (a vehicle or buoy) broadcasting its GPS
position and simultaneous one-way travel time range (OWTT)
to multiple AUVs. We discuss why our approach is scalable
as well as robust to modem transmission failure. Results are
provided for an ocean experiment using a Hydroid REMUS
100 AUV co-operating with one of two craft: an autonomous
surface vehicle (ASV) and a manned support vessel. During
these experiments the ranging portion of the algorithm ran online
on-board the AUV. Extension of the paradigm to multiple
missions via the optimization of successive survey missions (and
the resultant sonar mosaics) is also demonstrated.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://ras.papercept.net/conferences/scripts/abstract.pl?ConfID=34&Number=1555
