Towards Fast Correspondence-Free Odometry Using Multiple FMCW Lidars
Bibliographic record
Abstract
3D FMCW lidars return relative velocity measurements via the Doppler effect, which provides a new form of information for motion estimation. In our prior work, we proposed an odometry method that avoids the conventional ICP-based approach and uses the Doppler velocity measurements in a correspondence-free way. The caveat is that the angular velocity is not observable from the Doppler measurements of a single lidar, for which we compensate by using a gyroscope. This paper is an extension of our prior work that furthers our experiments using multiple FMCW lidars. We first present better odometry performance using an improved calibration for the Doppler measurement bias and noise, and additionally show how the Expectation-Maximization algorithm can be applied to calibrate without the groundtruth trajectory. We then show improved results on our correspondence-free odometry when given access to multiple lidars and gyroscopes. We also present preliminary results without gyroscope sensors to validate our prior algebraic study that the 6-degrees-of-freedom motion is still observable with multiple lidars. While we show that odometry performance without gyroscopes is not yet as accurate, we compare against simulated measurements to motivate the potential of Doppler-only lidar odometry as a future research direction.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".