Lighting‐invariant Visual Teach and Repeat Using Appearance‐based Lidar
Bibliographic record
Abstract
Visual Teach and Repeat (VT&R) is an effective method to enable a vehicle to repeat any previously driven route using just a visual sensor and without a global positioning system. However, one of the major challenges in recognizing previously visited locations is lighting change, as this can drastically alter the appearance of the scene. In an effort to achieve lighting invariance, this paper details the design of a VT&R system that uses a laser scanner as the primary sensor. Unlike a traditional scan‐matching approach, we apply appearance‐based computer vision techniques to laser intensity images for motion estimation, providing us the benefit of lighting invariance. Field tests were conducted in an outdoor, planetary analogue environment, over an entire diurnal cycle, repeating a 1.1 km route more than 10 times with an autonomy rate of 99.7% by distance. We describe, in detail, our experimental setup and results, as well as how we address the various off‐nominal scenarios related to feature‐poor environments, hardware failures, and estimation drift. An analysis on motion distortion and a comparison with a stereo‐based system is also presented. We show that even without motion compensation, our system is robust enough to repeat long‐range routes accurately and reliably. © 2012 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
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 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".