Non-Gaussian SLAM utilizing Synthetic Aperture Sonar
Author(s)
Cheung, Mei Yi; Fourie, Dehann; Rypkema, Nicholas Rahardiyan; Teixeira, Pedro V.; Schmidt, Henrik; Leonard, John J; ... Show more Show less
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Synthetic Aperture Sonar (SAS) is a technique to improve the spatial resolution from a moving set of receivers by extending the array in time, increasing the effective array length and aperture. This technique is limited by the accuracy of the receiver position estimates, necessitating highly accurate, typically expensive aided-inertial navigation systems for submerged platforms. We leverage simultaneous localization and mapping to fuse acoustic and navigational measurements and obtain accurate pose estimates even without the benefit of absolute positioning for lengthy underwater missions. We demonstrate a method of formulating the well-known SAS problem in a SLAM framework, using acoustic data from hydrophones to simultaneously estimate platform and beacon position. An empirical probability distribution is computed from a conventional beamformer to correctly account for uncertainty in the acoustic measurements. The non-parametric method relieves the familiar Gaussian-only assumption currently used in the localization and mapping discipline and fits effectively into a factor graph formulation with conventional factors such as ground-truth priors and odometry. We present results from field experiments performed on the Charles River with an autonomous surface vehicle which demonstrate simultaneous localization of an unknown acoustic beacon and vehicle positioning, and provide comparison to GPS ground truths.
Date issued
2019-05Department
Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cheung, Mei Yi et al. "Non-Gaussian SLAM utilizing Synthetic Aperture Sonar." IEEE International Conference on Robotics and Automation: ICRA 2019, May 20-24, 2019, Montreal, Quebec, IEEE, 2019: 3457-63 ©2019 Author(s)
Version: Author's final manuscript
ISBN
978-1-5386-6027-0
ISSN
2577-087X