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VINet : Visual-inertial odometry as a sequence-to-sequence learning problem
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Clark, Ronald, Wang, Sen, Wen, Hongkai, Markham, Andrew and Trigoni, Niki (2017) VINet : Visual-inertial odometry as a sequence-to-sequence learning problem. In: Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, 4-9 Feb 2017. Published in: Proceedings of Thirty-First AAAI Conference on Artificial Intelligence pp. 3995-4001. ISBN 9781577357834. doi:10.5555/3298023.3298149
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Official URL: https://dopi.org/10.5555/3298023.3298149
Abstract
In this paper we present VINet - a sequence-to-sequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first end-to-end trainable method for monocular visual- inertial odometry. Our method has numerous advantages over traditional approaches. Specifically, it eliminates the need for tedious manual synchronization of the camera and IMU as well as eliminating the need for manual calibration between the IMU and camera. A further advantage is that our model naturally and elegantly incorporates domain specific information which significantly mitigates drift. We show that our approach is competitive with state-of-the-art traditional methods when accurate calibration data is available and can be trained to outperform them in the presence of calibration and synchronization errors.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Series Name: | AAAI Conference on Artificial Intelligence | ||||||
| Journal or Publication Title: | Proceedings of Thirty-First AAAI Conference on Artificial Intelligence | ||||||
| Publisher: | AAAI | ||||||
| ISBN: | 9781577357834 | ||||||
| Official Date: | 12 February 2017 | ||||||
| Dates: |
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| Page Range: | pp. 3995-4001 | ||||||
| DOI: | 10.5555/3298023.3298149 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | Thirty-First AAAI Conference on Artificial Intelligence | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | San Francisco, California | ||||||
| Date(s) of Event: | 4-9 Feb 2017 | ||||||
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| Open Access Version: |
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