An Automatic Registration Approach to Laser Point Sets Based on Multidiscriminant Parameter Extraction
Bibliographic record
Abstract
The iterative closest point (ICP) algorithm is one of the most widely used methods for point sets' registration. However, ICP is very sensitive to the selection of initial points and is easy to fall into local optimum. To address this problem, many techniques have been developed. In this study, a two-step registration method is proposed for two 3-D point sets' registration, which is achieved by a combination of rough and fine registrations. Specifically, a multidiscriminant parameter feature (MDPF) extraction approach is developed and embedded into the rough registration stage in order to find new corresponding point pairs for the fine registration. Three geometric features are chosen after experimental investigation for key points selection. By using the threshold discriminant condition to determine the key points and the distance constraint to eliminate the wrong point pairs, the feature points can be extracted, and the final transform parameters can be derived based on these feature points. In order to improve the computational efficiency for fine registration, the center of gravity is created to find the closest point in solving the transformation matrix, which is especially beneficial for registering complex surfaces. Experimental results show that the proposed method outperforms the traditional ICP approach and some typical existing improved algorithms in terms of the root-mean-square error (RMSE), the total number of feature points, and the execution time. In particular, the performance is improved 40% in terms of the RMSE and 50% in terms of the execution time in comparison with ICP on some benchmark data sets. Experiments also demonstrate that reliable reconstruction results can be obtained for both real outdoor and indoor environments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".