DynaNav-SVO: Dynamic Stereo Visual Odometry With Semantic-Aware Perception for Autonomous Navigation
Bibliographic record
Abstract
Conventional visual navigation methods presume scene stability and encounter challenges due to moving objects in highly dynamic environments. We propose DynaNav-SVO, a stereo visual odometry (VO) framework, which semantically detects and constructs a region-of-interest (ROI) by focusing on a-priori urban fixed elements for reliable feature extraction and subsequently estimates vehicle pose. The outcome is a static map with minimal outliers and is used for state estimation in dynamic scenes and perceptually degraded conditions. This map enhances computational efficiency due to the reduced size of the new static mask (as confirmed in several experiments), compared to the existing visual simultaneous localization and mapping (vSLAM) solutions. To refine the estimated pose, a back-end module selects a moving horizon of frames, generates a covisibility graph for data association, and optimizes a structure-from-motion program using local bundle adjustment. Finally, the performance of the framework is experimentally evaluated using a test vehicle in highly-dynamic urban settings and under adverse weather conditions with degraded visual perception with varying sequence lengths. The experiments confirm excellent performance in terms of estimation accuracy and computational efficiency for autonomous navigation compared to existing vSLAM 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".