Optimal Robot Pose Estimation Using Scan Matching by Turning Function
Bibliographic record
Abstract
The turning function is a tool in image processing that measures the difference between two polygonal shapes. We propose a localization algorithm for the optimal pose estimation of autonomous mobile robots using the scan-matching method based on the turning function algorithm. There are several methodologies aimed at moving the robots in the right way and carrying out their missions well, which involves the integration of localization and control. In the proposed method, the localization problem is implemented in the form of an optimization problem. Afterwards, the turning function algorithm and the simplex method are applied to estimate the localization and orientation of the robot. The proposed algorithm first receives the polygons extracted from two sensors’ data and then allocates a histogram to each sensor scan. This algorithm attempts to maximize the similarity of the two histograms by converting them to a unified coordinate system. In this way, the estimate of the difference between the two situations is calculated. In more detail, the main objective of this study is to provide an algorithm aimed at reducing errors in the localization and orientation of mobile robots. The simulation results indicate the great performance of this algorithm. Experimental results on simulated and real datasets show that the proposed algorithms achieve better results in terms of both position and orientation metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".