Toward robust visual odometry using prior 2D map information and multiple hypothesis particle filtering

Edwards, S., Mihaylova, L. orcid.org/0000-0001-5856-2223, Aitken, J. orcid.org/0000-0003-4204-4020 et al. (1 more author) (2021) Toward robust visual odometry using prior 2D map information and multiple hypothesis particle filtering. In: Fox, C., Gao, J., Esfahani, G.H., Saaj, M., Hanheide, M. and Parsons, S., (eds.) Towards Autonomous Robotic Systems; 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8–10, 2021, Proceedings. Towards Autonomous Robotic Systems Conference (TAROS), 08-10 Sep 2021, Lincoln, UK (virtual conference). Lecture Notes in Computer Science (13054). Springer, Cham , pp. 188-192. ISBN 9783030891763

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Fox, C.
  • Gao, J.
  • Esfahani, G.H.
  • Saaj, M.
  • Hanheide, M.
  • Parsons, S.
Copyright, Publisher and Additional Information:

© 2021 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in TAROS 2021 Proceedings. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Visual odometry; Deep learning; Multiple Hypothesis; Particle filter; Map prior
Dates:
  • Published: 31 October 2021
  • Published (online): 31 October 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 06 Dec 2021 13:48
Last Modified: 06 Dec 2021 13:48
Status: Published
Publisher: Springer, Cham
Series Name: Lecture Notes in Computer Science
Refereed: Yes
Identification Number: 10.1007/978-3-030-89177-0_19
Related URLs:
Open Archives Initiative ID (OAI ID):

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