Towards End-to-end Learning of Visual Inertial Odometry with an EKF
Why this work is in the frame
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Bibliographic record
Abstract
Classical visual-inertial fusion relies heavily on manually crafted image processing pipelines, which are prone to failure in situations with rapid motion and texture-less scenes. While end-to-end learning methods show promising results in addressing these limitations, embedding domain knowledge in the form of classical estimation processes within the end-to-end learning architecture has the potential of combining the best of both worlds. In this paper, we propose the first end-to-end trainable visual-inertial odometry (VIO) algorithm that leverages a robo-centric Extended Kalman Filter (EKF). The EKF propagates states through a known inertial measurement unit (IMU) kinematics model and accepts relative pose measurements and uncertainties from a deep network as updates. The system is fully differentiable and can be trained end-to-end through backpropagation. Our method achieves a translation error of 1.27% on the KITTI odometry dataset, which is competitive among classical and learning VIO methods. The implementation is publicly available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it