Minimal solvers for indoor UAV positioning
(2021) 25th International Conference on Pattern Recognition, ICPR 2020 In Proceedings - International Conference on Pattern Recognition p.1136-1143- Abstract
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles (UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated... (More)
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles (UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art approaches. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other solvers, and yields superior motion estimation.
(Less)
- author
- Örnhag, Marcus Valtonen LU ; Persson, Patrik LU ; Wadenbäck, Mårten ; Aström, Kalle LU and Heyden, Anders LU
- organization
- publishing date
- 2021-05-05
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2020 25th International Conference on Pattern Recognition (ICPR)
- series title
- Proceedings - International Conference on Pattern Recognition
- article number
- 9412279
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 25th International Conference on Pattern Recognition, ICPR 2020
- conference location
- Virtual, Milan, Italy
- conference dates
- 2021-01-10 - 2021-01-15
- external identifiers
-
- scopus:85104180521
- ISSN
- 1051-4651
- ISBN
- 9781728188089
- DOI
- 10.1109/ICPR48806.2021.9412279
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: ACKNOWLEDGEMENTS We thank Ding et al. for providing their implementations. This work was partially supported by the Swedish Research Council (grant no. 2015-05639), the strategic research projects ELLIIT and eSSENCE, the Swedish Foundation for Strategic Research project, Semantic Mapping and Visual Navigation for Smart Robots (grant no. RIT15-0038), and Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation. Publisher Copyright: © 2020 IEEE
- id
- f04aceeb-69bb-442d-b44c-e54742b61609
- date added to LUP
- 2021-11-29 08:09:35
- date last changed
- 2023-12-07 23:00:39
@inproceedings{f04aceeb-69bb-442d-b44c-e54742b61609, abstract = {{<p>In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles (UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art approaches. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other solvers, and yields superior motion estimation.</p>}}, author = {{Örnhag, Marcus Valtonen and Persson, Patrik and Wadenbäck, Mårten and Aström, Kalle and Heyden, Anders}}, booktitle = {{2020 25th International Conference on Pattern Recognition (ICPR)}}, isbn = {{9781728188089}}, issn = {{1051-4651}}, language = {{eng}}, month = {{05}}, pages = {{1136--1143}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{Proceedings - International Conference on Pattern Recognition}}, title = {{Minimal solvers for indoor UAV positioning}}, url = {{http://dx.doi.org/10.1109/ICPR48806.2021.9412279}}, doi = {{10.1109/ICPR48806.2021.9412279}}, year = {{2021}}, }