The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints
Open accessAbstract
This paper presents an self-supervised deep learning network for monocular visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory estimation by directly merging 2D optical flow feature (OFF) and Inertial Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and dense 3D point cloud of each scene by using stereo sequences, and then obtains 3D geometric constraints including 3D optical flow and 6-DoF pose as supervisory signals. Note that such 3D optical flow shows robustness and accuracy to dynamic objects and textureless environments. In DeepVIO training, 2D optical flow network is constrained by the projection of its corresponding 3D optical flow, and LSTM-style IMU preintegration network and the fusion network are learned by minimizing the loss functions from ego-motion constraints. Furthermore, we employ an IMU status update scheme to improve IMU pose estimation through updating the additional gyroscope and accelerometer bias. The experimental results on KITTI and EuRoC datasets show that DeepVIO outperforms state-of-the-art learning based methods in terms of accuracy and data adaptability. Compared to the traditional methods, DeepVIO reduces the impacts of inaccurate Camera-IMU calibrations, unsynchronized and missing data.
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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 3×
“For example, the direct-based representative DSO [3] and the feature-based representative ORB-SLAM [4] both achieve very high localization accuracy in the large-scale environment, and real-time performance with commercial CPUs.”
BackgroundA Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
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Flownet: Learning optical flow with convolutional networks
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
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Unsupervised Monocular Depth Estimation with Left-Right Consistency
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
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Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
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Unsupervised Learning of Depth and Ego-Motion from Video
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Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
A survey of state-of-the-art on visual SLAM
Kazerouni, I. A., Fitzgerald, L., Dooly, G., & Toal, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117734 · Cited by 420
An Overview on Visual SLAM: From Tradition to Semantic
Chen, W.-F., Shang, G., Ji, A.-H., Zhou, C., Wang, X., Xu, C., Li, Z., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14133010 · Cited by 217
Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review
Niloy, M. A., Shama, A., Chakrabortty, R. K., Ryan, M. J., Badal, F. R., Tasneem, Z., Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3062557 · Cited by 172
Deep Learning for Inertial Positioning: A Survey
Chen, C., & Pan, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3381161 · Cited by 159
Metadata
- Authors
- Han, L., Lin, Y., Du, G., & Lian, S.
- Affiliations
- CloudMinds Technologies Inc,AI Department,Beijing,China,100102
- Venue
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 6906-6913
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W3003233851
- Cited by
- 125
- References
- 35
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 19 · 2023: 27 · 2022: 17 · 2021: 14 · 2020: 8 · 2019: 3
- FWCI
- 19.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- self-supervised learning, 3D optical flow, IMU pre-integration, IMU bias estimation, sensor fusion, stereo vision
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