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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".