One-to-one feature matching with inaccurate maps
Bibliographic record
Abstract
In the problems of localization using inaccurate maps, navigation agents have to match available information from sensors to maps in order to find their locations. A map contains a set of constraints that can be expressed in the form of a graphical model that matching algorithm has to satisfy. There are two generally categories of constraints: absolute and relative. We propose a relaxation-based algorithm for the NP-hard problem of one-to-one feature matching with absolute and relative constraints. The algorithm is a combination between relaxation labeling and the Kuhn-Munkres method where the former is known for its highly parallel structure imitated the human visual process. To test the performance, we applied the algorithm in a robotics application where the objective is to match range scanner features to those in inaccurate template maps provided by humans. Our experiments show that the proposed algorithm can achieve qualified matching results in artificial and real situations.
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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 | 0.001 |
| Open science | 0.001 | 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".