Cost-effective active localization technique for mobile robots
Why this work is in the frame
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Bibliographic record
Abstract
Mobile robot localization is the problem of determining the position of a mobile robot from sensor data. Active localization provides setting the robot's motion direction and determining the pointing direction of the sensors during localization so as to most efficiently localize the robot. This paper proposes an active localization approach that employs Monte Carlo Localization, which is based on particle filters. The technique offers two main advantages. 1) The framework applies a different way of initializing the particles that helps to reduce some steps of localization, and 2) a new resampling scheme is used to reduce the cost of localization and solve the kidnapped robot problem. Experimental results show that the probability of robot successfully localize itself is considerably high, i.e. robot can recover from failure and localize itself based on new sensor data and reduction of cost is noticeable.
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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