Gaussian Process Gauss-Newton for 3D laser-based Visual Odometry
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
In this paper, we present a method for obtaining Visual Odometry (VO) estimates using a scanning laser rangefinder. Though common VO implementations utilize stereo camera imagery, cameras are dependent on ambient light. In contrast, actively-illuminated sensors such as laser rangefinders work in a variety of lighting conditions, including full darkness. We leverage previous successes by applying sparse appearance-based methods to laser intensity images, and address the issue of motion distortion by considering the estimation problem in continuous time. This is facilitated by Gaussian Process Gauss-Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. We include a concise derivation of GPGN, along with details on the extension to three-dimensions (3D). Validation of the 3D laser-based VO framework is provided using 1.1km of experimental data, which was gathered by a field robot equipped with a two-axis scanning lidar.
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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 | 0.000 |
| 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 it