Bibliographic record
Abstract
Visual odometry (VO) is a highly efficient and powerful 6D motion estimation technique; state-of-the-art bundle adjustment algorithms now optimize over several frames of temporally tracked, appearance-based features in real time. It is well known that the temporal feature correspondence process is highly prone to mismatches. The standard technique used for outlier rejection in this process is random sample consensus (RANSAC), which is an iterative and non-deterministic process used to find the parameters of a mathematical model that best describe a likely set of inliers. The traditional model used for RANSAC in the visual odometry pipeline is a rigid transformation between two camera poses; this model has long assumed the use of an imaging sensor with a global shutter. In order to use imaging sensors that do not operate with a global shutter, it is proposed that the RANSAC algorithm be modified to use a constant-camera-velocity model. Specifically, this paper investigates the use of a two-axis scanning lidar in the visual-odometry pipeline. Images are formed using lidar intensity data, and due to the scanning-while-moving nature of the lidar, the behaviour of the sensor resembles that of a slow rolling-shutter camera. We formulate a Motion-Compensated RANSAC algorithm that uses a constant-velocity model and the individual timestamp of each extracted feature. The algorithm is validated using 6880 lidar frames with a resolution of 480 × 360, captured at 2 Hz, over a 1.1 km traversal. Our results show that the new algorithm results in far more inlying feature tracks for rolling-shutter-type images and ultimately higher-accuracy VO results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".