Non-Gaussian SLAM utilizing Synthetic Aperture Sonar
Name
cheung_icra2019.pdf
Description
Accepted version
Size
2.78 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Cheung, Mei Yi
Fourie, Dehann
Rypkema, Nicholas Rahardiyan
Teixeira, Pedro V.
Schmidt, Henrik
Leonard, John J
Date Issued
May 2019
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8793536
