Expanding the Limits of Vision‐based Localization for Long‐term Route‐following Autonomy
Bibliographic record
Abstract
Vision‐based, autonomous, route‐following algorithms enable robots to autonomously repeat manually driven routes over long distances. Through the use of inexpensive, commercial vision sensors, these algorithms have the potential to enable robotic applications across multiple industries. However, in order to extend these algorithms to long‐term autonomy, they must be able to operate over long periods of time. This poses a difficult challenge for vision‐based systems in unstructured and outdoor environments, where appearance is highly variable. While many techniques have been developed to perform localization across extreme appearance change, most are not suitable or untested for vision‐in‐the‐loop systems such as autonomous route following, which requires continuous metric localization to keep the robot driving. In this paper, we present a vision‐based, autonomous, route‐following algorithm that combines multiple channels of information during localization to increase robustness against daily appearance change such as lighting. We explore this multichannel visual teach and repeat framework by adding the following channels of information to the basic single‐camera, gray‐scale, localization pipeline: images that are resistant to lighting change and images from additional stereo cameras to increase the algorithm's field of view. Using these methods, we demonstrate robustness against appearance change through extensive field deployments spanning over 26 km with an autonomy rate greater than 99.9%. We furthermore discuss the limits of this system when subjected to harsh environmental conditions by investigating keypoint match degradation through time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".