A cost effective probabilistic approach to localization and mapping
Bibliographic record
Abstract
Localization and mapping in robotics are preliminary but challenging problems. A learning approach must be followed by a robot to understand its environment and perform data association before it accomplishes any other tasks. In this paper, we describe a novel combination of techniques to map the environmental boundaries traced by the robot and localize it inside the bounded region. This is an effort established using only an iRobot educational package and no expensive high-end external sensor. This method may be treated as a solution for mapping and localization in a static environment with a few low cost IR sensors. In the proposed approach, we trace the robot's movement in an arbitrary shaped bounded region and map the same using coastal rule wall following technique and the method of least squares. A full traversal of robot maps the boundary and the robot is localized in the environment using particle filter approach and computational geometry. Also, we studied the effect of localizing a kidnapped robot once the map is known.
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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 | 0.000 |
| 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 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".