On the Tightness of Semidefinite Relaxations for Rotation Estimation
(2022) In Journal of Mathematical Imaging and Vision 64(1). p.57-67- Abstract
Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding. A general framework based on tools from algebraic geometry is introduced for analyzing the power of semidefinite relaxations of problems with quadratic objective functions and rotational constraints. Applications include registration, hand–eye calibration, and rotation averaging. We characterize the extreme points and show that... (More)
Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding. A general framework based on tools from algebraic geometry is introduced for analyzing the power of semidefinite relaxations of problems with quadratic objective functions and rotational constraints. Applications include registration, hand–eye calibration, and rotation averaging. We characterize the extreme points and show that there exist failure cases for which the relaxation is not tight, even in the case of a single rotation. We also show that some problem classes are always tight given an appropriate parametrization. Our theoretical findings are accompanied with numerical simulations, providing further evidence and understanding of the results.
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- author
- Brynte, Lucas ; Larsson, Viktor ; Iglesias, José Pedro ; Olsson, Carl LU and Kahl, Fredrik LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Algebraic geometry, Almost minimal varieties, Duality, Rotation estimation, SDP relaxations, Sum-of-squares
- in
- Journal of Mathematical Imaging and Vision
- volume
- 64
- issue
- 1
- pages
- 57 - 67
- publisher
- Springer
- external identifiers
-
- scopus:85116735145
- ISSN
- 0924-9907
- DOI
- 10.1007/s10851-021-01054-y
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021, The Author(s).
- id
- 2c933d89-f9a3-430d-80f9-589246fa5591
- date added to LUP
- 2021-11-12 12:43:49
- date last changed
- 2022-06-29 12:55:51
@article{2c933d89-f9a3-430d-80f9-589246fa5591, abstract = {{<p>Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding. A general framework based on tools from algebraic geometry is introduced for analyzing the power of semidefinite relaxations of problems with quadratic objective functions and rotational constraints. Applications include registration, hand–eye calibration, and rotation averaging. We characterize the extreme points and show that there exist failure cases for which the relaxation is not tight, even in the case of a single rotation. We also show that some problem classes are always tight given an appropriate parametrization. Our theoretical findings are accompanied with numerical simulations, providing further evidence and understanding of the results.</p>}}, author = {{Brynte, Lucas and Larsson, Viktor and Iglesias, José Pedro and Olsson, Carl and Kahl, Fredrik}}, issn = {{0924-9907}}, keywords = {{Algebraic geometry; Almost minimal varieties; Duality; Rotation estimation; SDP relaxations; Sum-of-squares}}, language = {{eng}}, number = {{1}}, pages = {{57--67}}, publisher = {{Springer}}, series = {{Journal of Mathematical Imaging and Vision}}, title = {{On the Tightness of Semidefinite Relaxations for Rotation Estimation}}, url = {{http://dx.doi.org/10.1007/s10851-021-01054-y}}, doi = {{10.1007/s10851-021-01054-y}}, volume = {{64}}, year = {{2022}}, }