A nonparametric learning approach to vision based mobile robot localization
Bibliographic record
Abstract
A nonparametric learning algorithm is used to build a robust mapping between an image obtained from a mobile robot's on-board camera, and the robot's current position. The mapping uses 19,200 unprocessed pixel values (160 by 120 pixel image). Because the learning algorithm is nonparametric, it uses the learning data obtained from these raw pixel values to automatically choose a structure for the mapping without human intervention, or any a priori assumptions about what type of image features should be used. The learning data consisting of a series example image inputs and corresponding position values, is collected in a calibration phase where the robot randomly traverses its intended workspace. This process of building visual localization maps for mobile robots is completely general and can be applied to any implementation which uses on-board cameras. We demonstrate the feasibility of this approach on a mobile platform performing in a robotics laboratory workspace. This workspace is visually cluttered, with humans and other objects continually moving within the robot's environment. The mapping learned in this environment is robust to these dynamic visual features and consistently reports timely localization information (at greater than 7 Hz) to within acceptable limits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".