Research map / 3D vision / Depth and stereo
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train VINet with calibration-augmented data | VINet · EuRoC MAV, calibration errors from 0° to 15° | error: 3.0671 -> 0.5178 | Helped |
| Train VINet with calibration-augmented data instead of calibrated data only | VINet · EuRoC MAV, 0° calibration error | error: 0.1751 -> 0.1842 | Hurt |
| Use joint frame-to-frame and concatenated-pose losses instead of either loss alone (ablation) | VINet · KITTI Seq-00 training and validation | training and validation error | Helped |
| Train only with the frame-to-frame loss instead of joint losses (ablation) | VINet · KITTI Seq-00 training and validation | training and validation error | Hurt |
| Train only with the full concatenated-pose loss instead of joint losses (ablation) | VINet · KITTI Seq-00 training and validation | training and validation error | Hurt |
| Use vision-only input instead of visual-inertial input (ablation) | VINet · KITTI odometry, sequences 1-10 training and 11 test | translation error | Hurt |
Closest papers by meaning
- Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (CVPR 2024 · similarity 0.89)
- LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry (CVPR 2024 · similarity 0.86)
- Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation (NeurIPS 2024 · similarity 0.86)
- EqNIO: Subequivariant Neural Inertial Odometry (ICLR 2025 · similarity 0.85)
- Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry (AAAI 2025 · similarity 0.84)
- Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics (CVPR 2025 · similarity 0.84)
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