Learning Place-and-Time-Dependent Binary Descriptors for Long-Term Visual Localization
Why this work is in the frame
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Bibliographic record
Abstract
Vision-based navigation is extremely susceptible to natural scene changes. This can result in localization failures in less than a few hours after map creation. To combat short-term illumination changes as well as long-term seasonal variations, we propose using a place-and-time-dependent binary descriptor that adapts to different scenarios in an online fashion. This is achieved by extending the GRIEF [6] evolution algorithm in two ways: correspondence generation using a known pose change and the inclusion of LATCH triplets in addition to BRIEF comparisons for descriptor generation. We show the adaptive descriptor outperforms a single descriptor scheme for localization within a single-experience Visual Teach and Repeat (VT&R) system while maintaining the efficiency of binary descriptors. By adapting the description function to different environmental conditions, it allows the system to operate for a longer period before a new experience is required. In the presence of extreme illumination changes from day to night, we obtain 40% more inlier matches compared to SURF. In the case of seasonal variations, a 70% increase is demonstrated. The increased correspondences result in more localizable sections along the paths, amounting to a 25% and 150% increase in the lighting and seasonal cases, respectively.
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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