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Cited by 922

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems

Open access
Forster, Christian, Zhang, Zichao, Gassner, Michael, Werlberger, Manuel, Scaramuzza, Davide·2017·IEEE Transactions on Robotics
DOI 10.1109/tro.2016.2623335
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Abstract

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.

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Metadata

Authors
Forster, C., Zhang, Z., Gassner, M., Werlberger, M., & Scaramuzza, D.
Venue
IEEE Transactions on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-04
Volume
33
Issue
2
Pages
249-265
ISSN
  • 1552-3098 (ISSN-L)
  • 1941-0468
DOI
10.1109/tro.2016.2623335
OpenAlex
W2564632156
Cited by
922
References
14
FWCI
87.48
Type
journal article
Language
en
Open access
green
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Funders
  • Swiss National Foundation (200021-143607)
  • Swarm of Flying Cameras
  • National Center of Competence in Research Robotics
  • UZH Forschungskredit
  • SNSF-ERC Starting

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CoChat. (2026). Citation check: SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems. https://app.cochat.ai/papers/10.1109/tro.2016.2623335
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Forster, C., Zhang, Z., Gassner, M., Werlberger, M., & Scaramuzza, D. (2017). SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2016.2623335

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