Robust Monocular Visual Teach and Repeat Aided by Local Ground Planarity and Color‐constant Imagery
Why this work is in the frame
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Bibliographic record
Abstract
Visual Teach and Repeat (VT&R) allows an autonomous vehicle to accurately repeat a previously traversed route using only vision sensors. Most VT&R systems rely on natively three‐dimensional (3D) sensors such as stereo cameras for mapping and localization, but many existing mobile robots are equipped with only 2D monocular vision, typically for teleoperation. In this paper, we extend VT&R to the most basic sensor configuration—a single monocular camera. We show that kilometer‐scale route repetition can be achieved with centimeter‐level accuracy by approximating the local ground surface near the vehicle as a plane with some uncertainty. This allows our system to recover absolute scale from the known position and orientation of the camera relative to the vehicle, which simplifies threshold‐based outlier rejection and the estimation and control of lateral path‐tracking error—essential components of high‐accuracy route repetition. We enhance the robustness of our monocular VT&R system to common failure cases through the use of color‐constant imagery, which provides it with a degree of resistance to lighting changes and moving shadows where keypoint matching on standard gray images tends to struggle. Through extensive testing on a combined 30 km of autonomous navigation data collected on multiple vehicles in a variety of highly nonplanar terrestrial and planetary‐analogue environments, we demonstrate that our system is capable of achieving route‐repetition accuracy on par with its stereo counterpart, with only a modest tradeoff in robustness.
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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