Using RGB Information to Improve NDT Distribution Generation and Registration Convergence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned vehicles are becoming an inevitability in our society and with them comes the need for highly robust and accurate algorithms to perform their critical functions, such as localization and mapping. The proliferation of these robots into wide spread use requires a generalized, robust SLAM solution. This paper proposes an improved NDT algorithm, which is capable of performing robust, accurate localization and mapping in an broad spectrum of possible environments and with a multitude of different sensors. The method uses a color greedy cluster approach to cluster points and generate Gaussian distributions and then uses an exhaustive color weighted distribution to distribution cost function to optimize the scan alignment. With the addition of these key features to the NDT framework the method is capable of providing accurate results with minimal computation time. Evaluation is performed on both the Freiburg and Ford datasets to demonstrate a multitude of environments and shows robust registration throughout a wide range of environments and viewpoints.
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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 | 0.001 |
| 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