DeepTAM: Deep Tracking and Mapping
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VIOLearner is the second deep visual-inertial approach, following VINet.
“To our knowledge, we are only the second deep visual-inertial approach (the first being VINet of [2]).”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [2]
VINet was robust to temporal and spatial misalignments of an IMU and camera but required extrinsic calibration between them.
“While VINet showed robustness to temporal and spatial misalignments of an IMU and camera, it still required extrinsic calibration parameters between camera and IMU.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [2]
VINet was trained supervised and required ground-truth pose differences.
“In addition, VINet was trained in a supervised manner and thus required the ground truth pose differences for each exemplar in the training set which are not always readily available.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [2]
VINet was the first end-to-end trainable visual-inertial deep network.
“VINet [2] was the first end-to-end trainable visual-inertial deep network.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [2]
VIOLearner RGB-D outperforms the custom EKF with VISO2 used by the authors of [2].
“VIOLearner RGB-D outperforms their EKF with VISO2.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [2]
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“DL has also been successfully applied to visual odometry [24, 25].”
BackgroundMethodVisual Odometry Revisited: What Should Be Learnt?
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Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
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DF-VO: What Should Be Learnt for Visual Odometry?
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- Authors
- Zhou, H., Ummenhofer, B., & Brox, T.
- Affiliations
- University of Freiburg (DE)
- Corresponding author
- Zhou, Huizhong
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 851-868
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2887825894
- Cited by
- 226
- References
- 40
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 27 · 2023: 22 · 2022: 28 · 2021: 39 · 2020: 44 · 2019: 40 · 2018: 2
- FWCI
- 54.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- depth map estimation, cost volume, 6D pose estimation, dense depth estimation, RGBD tracking
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