ICRA 2011 Paper Abstract


Paper TuP212.5

Fallon, Maurice (MIT), Kaess, Michael (MIT), Johannsson, Hordur (MIT), Leonard, John (MIT)

Efficient AUV Navigation Fusing Acoustic Ranging and Side-Scan Sonar

Scheduled for presentation during the Regular Sessions "Field and Underwater Robotics II" (TuP212), Tuesday, May 10, 2011, 16:25−16:40, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Marine Robotics, Sensor Fusion, SLAM


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 on-line on-board the AUV. Extension of the paradigm to multiple missions via the optimization of successive survey missions (and



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