Eye on the Sky: An Upward-Looking Monocular Teach-and-Repeat System for Indoor Environments
Bibliographic record
Abstract
Visual Teach and Repeat (VT&R) allows a robotic vehicle to navigate autonomously along a network of paths in the presence of illumination and scene changes. Traditionally, the system uses a stereo camera as the primary sensor for triangulating visual landmarks and often operates in highly textured outdoor environments. In this paper, we modify the VT&R system to use a monocular pipeline under the same framework, but also target indoor operation as a demonstration of a low-cost VT&R solution for warehouse logistics in a visually difficult environment. Unlike previous monocular VT&R solutions, we make no assumptions about the nature of the scene (e.g., local ground planarity). This allows the system to be readily deployable on more vehicles in a wider range of environments. To test the system, and motivated by a warehouse navigation application, an upward pointing camera is mounted on a Clearpath Husky ground vehicle. We demonstrate the vehicle is able to navigate with a 99.6% autonomy rate using such a system during 1.1 kilometers of driving, with an average scene depth that varies from 8-16 meters. The cross-track deviation from the taught path is less than 0.5 meters over 90% of the path, reaching a maximum of 0.85 meters and an average of 0.26 meters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".