Towards End-to-end Learning of Monocular Visual-inertial Odometry with an Extended Kalman Filter
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 thesis, 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 competitive results among state of the art classical and learning based VIO methods on the KITTI dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".