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Trust Your IMU : Consequences of Ignoring the IMU Drift

Valtonen Ornhag, Marcus LU ; Persson, Patrik LU orcid ; Wadenback, Marten ; Astrom, Kalle LU orcid and Heyden, Anders LU orcid (2022) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2022-June. p.4467-4476
Abstract

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups.The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles... (More)

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups.The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://github.com/marcusvaltonen/DronePoseLib.1

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
series title
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
volume
2022-June
pages
10 pages
publisher
IEEE Computer Society
conference name
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
conference location
New Orleans, United States
conference dates
2022-06-19 - 2022-06-20
external identifiers
  • scopus:85137766516
ISSN
2160-7508
2160-7516
ISBN
9781665487399
DOI
10.1109/CVPRW56347.2022.00493
language
English
LU publication?
yes
id
4bef88bf-194e-4fac-a922-7801345d35a5
date added to LUP
2022-11-30 11:30:45
date last changed
2025-01-22 19:18:14
@inproceedings{4bef88bf-194e-4fac-a922-7801345d35a5,
  abstract     = {{<p>In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups.The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://github.com/marcusvaltonen/DronePoseLib.1</p>}},
  author       = {{Valtonen Ornhag, Marcus and Persson, Patrik and Wadenback, Marten and Astrom, Kalle and Heyden, Anders}},
  booktitle    = {{Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022}},
  isbn         = {{9781665487399}},
  issn         = {{2160-7508}},
  language     = {{eng}},
  pages        = {{4467--4476}},
  publisher    = {{IEEE Computer Society}},
  series       = {{IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}},
  title        = {{Trust Your IMU : Consequences of Ignoring the IMU Drift}},
  url          = {{http://dx.doi.org/10.1109/CVPRW56347.2022.00493}},
  doi          = {{10.1109/CVPRW56347.2022.00493}},
  volume       = {{2022-June}},
  year         = {{2022}},
}