The DOI matches its Crossref record; 62 of the 66 references it cites were found in a registry or an index.
Visual Odometry Revisited: What Should Be Learnt?
Open accessAbstract
In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning. Most existing VO/SLAM systems with superior performance are based on geometry and have to be carefully designed for different application scenarios. Moreover, most monocular systems suffer from scale-drift issue. Some recent deep learning works learn VO in an end-to-end manner but the performance of these deep systems is still not comparable to geometry-based methods. In this work, we revisit the basics of VO and explore the right way for integrating deep learning with epipolar geometry and Perspective-n-Point (PnP) method. Specifically, we train two convolutional neural networks (CNNs) for estimating single-view depths and two-view optical flows as intermediate outputs. With the deep predictions, we design a simple but robust frame-to-frame VO algorithm (DF-VO) which outperforms pure deep learning-based and geometry-based methods. More importantly, our system does not suffer from the scale-drift issue being aided by a scale consistent single-view depth CNN. Extensive experiments on KITTI dataset shows the robustness of our system and a detailed ablation study shows the effect of different factors in our system. Code is available at here: DF-VO.
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“As shown in (Zhan et al. 2020), DF-VO with this correspondence selection scheme has already outperformed existing VO/SLAM baselines.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Zhan et al. 2020
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
(2017) · Cited by 11,625
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · cited 9×
“One of the most successful and accurate full SLAM systems using a sparse (ORB) feature-based approach is ORB-SLAM2 [13], along with DSO [16], a direct keyframe-based sparse SLAM method.”
MethodInfluentialORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Research works printed but not linked: 2
- godard, 2019, Digging into self-supervised monocular depth prediction, IEEE International Conference on Computer Vision (ICCV)
- 10.1023/A:1007941100561 · DOI
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Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Tang, J., Ambrus, R., Guizilini, V., Pillai, S., Kim, H., Jensfelt, P., & Gaidon, A. (2019) · arXiv · doi:10.48550/arxiv.1912.03426 · Cited by 14
Metadata
- Authors
- Zhan, H., Weerasekera, C. S., Bian, J.-W., & Reid, I.
- Affiliations
- The University of Adelaide (AU)
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 4203-4210
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3091687063
- Cited by
- 201
- References
- 66
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 37 · 2023: 37 · 2022: 33 · 2021: 30 · 2020: 17
- FWCI
- 43.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Centre for Robotic Vision
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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
- monocular visual odometry, scale drift, single-view depth estimation, optical flow, epipolar geometry, perspective-n-point, convolutional neural networks
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