SVO: Fast semi-direct monocular visual odometry
Open accessAbstract
We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle state-estimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.
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VIOLearner RGB-D outperforms Zhan et al.'s deep VO approach and traditional VISO2 on KITTI sequences 09 and 10.
“Similarly, VIOLearner RGB-D outperforms the deep VO approach of Zhan et al. [30] and the traditional VISO2 on both sequences.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [30]
For KITTI, VIOLearner RGB outperforms SFMLearner and the network of Zhan et al.
“For KITTI, VIOLearner RGB outperforms approaches SFMLearner [33] and the network of Zhan et al. [30].”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [30]
For KITTI, VIOLearner (no IMU) outperforms SFMLearner and the network of Zhan et al. but is outperformed by ORB-SLAM-2, which uses full bundle adjustment.
“Compared to other vision-only approaches, for KITTI, VIOLearner (no IMU) outperforms approaches SFMLearner [33] and the network of Zhan et al. [30] and yet is outperformed by ORB-SLAM-2 which uses full bundle adjustment.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [30]
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- Authors
- Forster, C., Pizzoli, M., & Scaramuzza, D.
- Affiliations
- University of Zurich (CH)
- ORCID
- Scaramuzza, Davide
- Venue
- 2014 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 15-22
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1970504153
- Cited by
- 2,205
- References
- 33
- Citations by year
- 2026: 74 · 2025: 164 · 2024: 219 · 2023: 218 · 2022: 212 · 2021: 246 · 2020: 211 · 2019: 258 · 2018: 191 · 2017: 188 · 2016: 115 · 2015: 95 · 2014: 14
- FWCI
- 231.06
- Type
- conference paper
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- other-oa
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- visual odometry, monocular camera, pixel intensity, sub-pixel precision, probabilistic mapping, outlier removal, motion estimation, micro air vehicles, GPS-denied navigation
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