Enforcing the General Planar Motion Model : Bundle Adjustment for Planar Scenes
(2020) 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11996 LNCS. p.119-135- Abstract
In this paper we consider the case of planar motion, where a mobile platform equipped with two cameras moves freely on a planar surface. The cameras are assumed to be directed towards the floor, as well as being connected by a rigid body motion, which constrains the relative motion of the cameras and introduces new geometric constraints. In the existing literature, there are several algorithms available to obtain planar motion compatible homographies. These methods, however, do not minimise a physically meaningful quantity, which may lead to issues when tracking the mobile platform globally. As a remedy, we propose a bundle adjustment algorithm tailored for the specific problem geometry. Due to the new constrained model, general bundle... (More)
In this paper we consider the case of planar motion, where a mobile platform equipped with two cameras moves freely on a planar surface. The cameras are assumed to be directed towards the floor, as well as being connected by a rigid body motion, which constrains the relative motion of the cameras and introduces new geometric constraints. In the existing literature, there are several algorithms available to obtain planar motion compatible homographies. These methods, however, do not minimise a physically meaningful quantity, which may lead to issues when tracking the mobile platform globally. As a remedy, we propose a bundle adjustment algorithm tailored for the specific problem geometry. Due to the new constrained model, general bundle adjustment frameworks, compatible with the standard six degree of freedom model, are not directly applicable, and we propose an efficient method to reduce the computational complexity, by utilising the sparse structure of the problem. We explore the impact of different polynomial solvers on synthetic data, and highlight various trade-offs between speed and accuracy. Furthermore, on real data, the proposed method shows an improvement compared to generic methods not enforcing the general planar motion model.
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- author
- Valtonen Örnhag, Marcus LU and Wadenbäck, Mårten LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bundle adjustment, Planar motion, SLAM, Visual Odometry
- host publication
- Pattern Recognition Applications and Methods - 8th International Conference, ICPRAM 2019, Revised Selected Papers
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- De Marsico, Maria ; Sanniti di Baja, Gabriella and Fred, Ana
- volume
- 11996 LNCS
- pages
- 17 pages
- publisher
- Springer
- conference name
- 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019
- conference location
- Prague, Czech Republic
- conference dates
- 2019-02-19 - 2019-02-21
- external identifiers
-
- scopus:85079559362
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783030400132
- DOI
- 10.1007/978-3-030-40014-9_6
- language
- English
- LU publication?
- yes
- id
- da1ecf18-5412-46a0-ac15-428465dba720
- date added to LUP
- 2021-01-11 13:35:01
- date last changed
- 2024-07-11 06:23:40
@inproceedings{da1ecf18-5412-46a0-ac15-428465dba720,
abstract = {{<p>In this paper we consider the case of planar motion, where a mobile platform equipped with two cameras moves freely on a planar surface. The cameras are assumed to be directed towards the floor, as well as being connected by a rigid body motion, which constrains the relative motion of the cameras and introduces new geometric constraints. In the existing literature, there are several algorithms available to obtain planar motion compatible homographies. These methods, however, do not minimise a physically meaningful quantity, which may lead to issues when tracking the mobile platform globally. As a remedy, we propose a bundle adjustment algorithm tailored for the specific problem geometry. Due to the new constrained model, general bundle adjustment frameworks, compatible with the standard six degree of freedom model, are not directly applicable, and we propose an efficient method to reduce the computational complexity, by utilising the sparse structure of the problem. We explore the impact of different polynomial solvers on synthetic data, and highlight various trade-offs between speed and accuracy. Furthermore, on real data, the proposed method shows an improvement compared to generic methods not enforcing the general planar motion model.</p>}},
author = {{Valtonen Örnhag, Marcus and Wadenbäck, Mårten}},
booktitle = {{Pattern Recognition Applications and Methods - 8th International Conference, ICPRAM 2019, Revised Selected Papers}},
editor = {{De Marsico, Maria and Sanniti di Baja, Gabriella and Fred, Ana}},
isbn = {{9783030400132}},
issn = {{1611-3349}},
keywords = {{Bundle adjustment; Planar motion; SLAM; Visual Odometry}},
language = {{eng}},
pages = {{119--135}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Enforcing the General Planar Motion Model : Bundle Adjustment for Planar Scenes}},
url = {{http://dx.doi.org/10.1007/978-3-030-40014-9_6}},
doi = {{10.1007/978-3-030-40014-9_6}},
volume = {{11996 LNCS}},
year = {{2020}},
}