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MBA-VO : Motion blur aware visual odometry

Liu, Peidong ; Zuo, Xingxing ; Larsson, Viktor LU and Pollefeys, Marc (2021) 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 p.5530-5539
Abstract
Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera’s local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable... (More)
Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera’s local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur. Both the code and the datasets can be found from https://github.com/ethliup/MBA-VO. (Less)
Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
conference location
Virtual, Online, Canada
conference dates
2021-10-11 - 2021-10-17
external identifiers
  • scopus:85127437786
DOI
10.1109/ICCV48922.2021.00550
language
English
LU publication?
no
id
e53755ec-f3ec-48d9-ac10-2fbc6fcd7b0e
date added to LUP
2022-09-06 13:19:29
date last changed
2022-09-20 18:52:22
@inproceedings{e53755ec-f3ec-48d9-ac10-2fbc6fcd7b0e,
  abstract     = {{Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera’s local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur. Both the code and the datasets can be found from https://github.com/ethliup/MBA-VO.}},
  author       = {{Liu, Peidong and Zuo, Xingxing and Larsson, Viktor and Pollefeys, Marc}},
  booktitle    = {{2021 IEEE/CVF International Conference on Computer Vision (ICCV)}},
  language     = {{eng}},
  pages        = {{5530--5539}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{MBA-VO : Motion blur aware visual odometry}},
  url          = {{http://dx.doi.org/10.1109/ICCV48922.2021.00550}},
  doi          = {{10.1109/ICCV48922.2021.00550}},
  year         = {{2021}},
}