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Globally Optimal Relative Pose Estimation with Gravity Prior

Ding, Yaqing LU ; Barath, Daniel ; Yang, Jian ; Kong, Hui and Kukelova, Zuzana (2021) 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Abstract
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared... (More)
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions. (Less)
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author
; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
conference location
Virtual, Online, United States
conference dates
2021-06-19 - 2021-06-25
external identifiers
  • scopus:85121832015
ISBN
978-1-6654-4510-8
978-1-6654-4509-2
DOI
10.1109/CVPR46437.2021.00046
language
English
LU publication?
no
id
b468b126-838b-4fd3-8cb8-c4a173e21590
date added to LUP
2022-09-08 10:22:54
date last changed
2025-04-16 21:50:35
@inproceedings{b468b126-838b-4fd3-8cb8-c4a173e21590,
  abstract     = {{Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions.}},
  author       = {{Ding, Yaqing and Barath, Daniel and Yang, Jian and Kong, Hui and Kukelova, Zuzana}},
  booktitle    = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  isbn         = {{978-1-6654-4510-8}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Globally Optimal Relative Pose Estimation with Gravity Prior}},
  url          = {{http://dx.doi.org/10.1109/CVPR46437.2021.00046}},
  doi          = {{10.1109/CVPR46437.2021.00046}},
  year         = {{2021}},
}