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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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Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
Crupi, L., Cereda, E., & Palossi, D. (2024) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-76424-0_53 · Cited by 2
NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics
Griffiths, R., Naylor, J., & Dansereau, D. G. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160663 · Cited by 1
Neural Ranging Inertial Odometry
Wang, S., Shen, B., Wang, F., Cao, Y., Xiong, R., & Wang, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128550 · Cited by 1
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TSFNet: A Two-Stage Fusion Network for Visual–Inertial Odometry
Wang, S., Liang, Y., Lin, J., Gan, Y., Zhong, M., Yin, X., & Bao, P. (2025) · Mathematics · doi:10.3390/math13233842 · Cited by 1
SS-VIO: A Novel Cross Modal State Space Visual Inertial Odometry
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Localization for car-like vehicles using loosely coupled dual-GNSS integration
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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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