Zhang, Y., Ding, Y., Song, J. et al. (2 more authors) (2022) A fast Manhattan frame estimation method based on normal vectors. Journal of Field Robotics, 39 (5). pp. 557-579. ISSN 1556-4959
Abstract
In most human made scenes, such as high-rise urban city or indoor environment, the surface normal vectors or direction vectors are concentrated in three orthogonal principal directions. The scene of such a pattern is called Manhattan World (MW), and the coordinate frame formed by the three principal directions is called Manhattan Frame (MF). MF estimation methods have been applied to many different fields, such as scene reconstruction, Visual based Simultaneous Localization And Mapping (V-SLAM) and camera calibration. In this paper, we propose a novel MF estimation method based on a set of normal vectors. A cost function of normal vectors and MF axes is introduced based on the trigonometric function. For computational purpose, the cost function is significantly simplified by making use of vector dot and cross products, and the reduced cost function only involves 14 scalar parameters that need to be computed with O(n) complexity. The experimental results show that the proposed MF estimation method has excellent real-time performance and gives high accuracy on both the virtual and real-world benchmark datasets of different sizes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Wiley Periodicals LLC. This is an author-produced version of a paper subsequently published in Journal of Field Robotics. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Feb 2022 15:39 |
Last Modified: | 26 Feb 2023 01:13 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/rob.22064 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183320 |