Do We Need to Compensate for Motion Distortion and Doppler Effects in\n Spinning Radar Navigation?
Bibliographic record
Abstract
In order to tackle the challenge of unfavorable weather conditions such as\nrain and snow, radar is being revisited as a parallel sensing modality to\nvision and lidar. Recent works have made tremendous progress in applying\nspinning radar to odometry and place recognition. However, these works have so\nfar ignored the impact of motion distortion and Doppler effects on\nspinning-radar-based navigation, which may be significant in the self-driving\ncar domain where speeds can be high. In this work, we demonstrate the effect of\nthese distortions on radar odometry using the Oxford Radar RobotCar Dataset and\nmetric localization using our own data-taking platform. We revisit a\nlightweight estimator that can recover the motion between a pair of radar scans\nwhile accounting for both effects. Our conclusion is that both motion\ndistortion and the Doppler effect are significant in different aspects of\nspinning radar navigation, with the former more prominent than the latter. Code\nfor this project can be found at:\nhttps://github.com/keenan-burnett/yeti_radar_odometry\n
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".