Robustness Improvement of Using Pre-Trained Network in Visual Odometry for On-Road Driving
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".