Into Darkness: Visual Navigation Based on a Lidar-Intensity-Image Pipeline
Bibliographic record
Abstract
<p>Visual navigation of mobile robots has become a core capability that enables many interesting applications from planetary exploration to self-driving cars. While systems built on passive cameras have been shown to be robust in well-lit scenes, they cannot handle the range of conditions associated with a full diurnal cycle. Lidar, which is fairly invariant to ambient lighting conditions, offers one possible remedy to this problem. In this paper, we describe a visual navigation pipeline that exploits lidar’s ability to measure both range and intensity (a.k.a., reflectance) information. In particular, we use <em>lidar intensity images</em> (from a scanning-laser rangefinder) to carry out tasks such as <em>visual odometry</em> (VO) and <em>visual teach and repeat</em> (VT&R) in realtime, from full-light to full-dark conditions. This lighting invariance comes at the price of coping with motion distortion, owing to the scanning-while-moving nature of laser-based imagers. We present our results and lessons learned from the last few years of research in this area.</p>
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How this classification was reachedexpand
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".