Level-Headed: Evaluating Gimbal-Stabilised Visual Teach and Repeat for Improved Localisation Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Operating in rough, unstructured terrain is an essential requirement for any truly field-deployable ground robot. Search-and-rescue, border patrol and agricultural work all require operation in environments with little established infrastructure for easy navigation. This presents challenges for sensor-based navigation such as vision, where erratic motion and feature-poor environments test feature tracking and hinder the performance of repeat matching of point features. For vision-based route-following methods such as Visual Teach and Repeat (VT&R), maintaining similar visual perspective of salient point features is critical for reliable odometry and accurate localisation over long periods. In this paper, we investigate a potential solution to these challenges by integrating a gimbaled camera with VT&R on a Grizzly Robotic Utility Vehicle (RUV) for testing at high speeds and in visually challenging environments. We examine the benefits and drawbacks of using an actively gimbaled camera to attenuate image motion and control viewpoint. We compare the use of a gimbaled camera to our traditional fixed stereo configuration and demonstrate cases of improved performance in Visual Odometry (VO), localisation and path following in several sets of outdoor experiments.
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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