Night Rider: Visual Odometry Using Headlights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual Odometry (VO) is a key enabling technology for mobile robotic systems that provides a relative motion estimate from a sequence of camera images. Cameras are comparatively inexpensive sensors, and provide large amounts of useful data, making them one of the most common sensors in mobile robotics. However, because they are passive, they are dependent on external lighting, which can restrict their usefulness. Using headlights as an alternate lighting source, this paper investigates outdoor stereo VO performance under all lighting conditions during nearly 10 km of driving over 30 hours. Challenges include limited visibility range, a dynamic light source, intensity hotspots, and others. Another large issue comes from blooming and lens flare at dawn and dusk, when the camera is looking directly into the sun. In our experiments, nighttime driving with headlights has a moderately increased error of 2.38% over 250 m compared to the daytime error of 1.5%. To the best of our knowledge this is the first quantitative study of VO performance at night using headlights.
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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