The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry
Bibliographic record
Abstract
A key element of many odometry pipelines using spinning frequency-modulated continuous-wave radar is the extraction of a point-cloud from the raw signal intensity returns. This extraction greatly impacts the overall performance of point-cloud-based odometry, but a consensus on which extractor performs best in which circumstances is missing. This paper provides a first-of-its-kind, comprehensive comparison of 13 common radar point-cloud extractors for the task of iterative closest point-based odometry in autonomous driving environments. Each extractor’s parameters are tuned and tested on two FMCW radar datasets using approximately 176 km of data from public roads. We find that the simplest, and fastest extractor, K-strongest, performs the best overall, outperforming the average by 13.59% and 24.94% on each dataset, respectively. In addition to an overall extractor recommendation, we highlight trends and note the substantial impact that the choice of extractor can have on the accuracy of odometry.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".