Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

VINet : Visual-inertial odometry as a sequence-to-sequence learning problem

Tools
- Tools
+ Tools

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

[img] PDF
WRAP_AAAI-17_Submission__2524.pdf - Accepted Version
Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer.

Download (2313Kb)
Official URL: https://dopi.org/10.5555/3298023.3298149

Request Changes to record.

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:
DateEvent
12 February 2017Published
11 November 2016Accepted
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
Related URLs:
  • Publisher
  • Publisher
Open Access Version:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us