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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Liu, J., Zheng, Q., Zhu, P., Zhang, X., & 宗艳桃 (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132498 · Cited by 1
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Fully Differentiable and Interpretable Model for VIO with 4 Trainable ParametersPreprint
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Neural Visual-Inertial Odometry with Integrated Mechanical Choreography
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Deep Visual-inertial odometry using adaptive visual data selection and External Memory Attention
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SATVIO: Stereo Attention-Based Visual Inertial Odometry
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Aleatoric Uncertainty Reduction in a Multisensor Navigation System Using Gated Recurrent Unit
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SWingL1: A Smoothly-Fused Wing-L1 Loss for Small-Error Sensitivity in Visual-Inertial Odometry
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Error Correction Using Bayesian GRU Network in Hybrid Visual Inertial Navigation System
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MamVIO: End-to-End Visual–Inertial Odometry via Mamba for Intelligent Vehicles
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Motion-Gated TCN Velocity Prior for Robust Dead-Reckoning Through DVL Outages in AUV Navigation
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Diff-VIO: A Diffusion Model-Based Pose Optimizer for Visual Inertial Odometry
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TOR-VIO: Time-Offset-Robust Self-Supervised Visual–Inertial Odometry
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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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