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Minimal solvers for indoor UAV positioning

Örnhag, Marcus Valtonen LU ; Persson, Patrik LU orcid ; Wadenbäck, Mårten ; Aström, Kalle LU orcid and Heyden, Anders LU orcid (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.

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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
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}},
}