Pose Interpolation for 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 two methods for obtaining visual odometry (VO) estimates using a scanning laser rangefinder. Although common VO implementations utilize stereo camera imagery, passive 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 we address the issue of motion distortion by considering the timestamps of the interest points detected in each image. To account for the unique timestamps, we introduce two estimator formulations. In the first method, we extend the conventional discrete‐time batch estimation formulation by introducing a novel frame‐to‐frame linear interpolation scheme, and in the second method, we consider the estimation problem by starting with a continuous‐time process model. This is facilitated by Gaussian process Gauss‐Newton (GPGN), an algorithm for nonparametric, continuous‐time, nonlinear, batch state estimation. Both laser‐based VO methods are compared and validated using datasets obtained by two experimental configurations. These datasets consist of 11 km of field data gathered by a high‐frame‐rate scanning lidar and a 365 m traverse using a sweeping planar laser rangefinder. Statistical analysis shows a 5.3% average translation error as a percentage of distance traveled for linear interpolation and 4.4% for GPGN in the high‐frame‐rate scenario.
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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