Robustness Improvement of Using Pre-Trained Network in Visual Odometry for On-Road Driving
Why this work is in the frame
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Bibliographic record
Abstract
Robustness in on-road driving Visual Odometry (VO) systems is critical, as it determines the reliable performance in various scenarios and environments. Especially with the development of data-driven technology, the combination of data-driven VO and model-based VO has achieved accurate tracking performance. However, the lack of generalization of pre-trained deep neural networks (DNN) limits the robustness of such a combination in unseen environments. In this study, we introduce a novel framework with appropriate usage of DNN prediction and improve the robustness in the self-driving application. Based on the characteristic of on-road self-driving motion and the DNN output, we propose a two-step optimization strategy with a variable degree of freedom (DoF), i.e., the use of two types of DoF representations during pose estimation. Specifically, our two-step optimization operates according to the residual of the optimization with the motion label classification from the pre-trained DNN, as well as our proposed Motion Evaluation by essential matrix construction. Experimental results show that our framework obtains better tracking accuracy than the existing methods.
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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 | 0.001 |
| 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