Streamlining Calibration: Eliminating Infrastructure and Certifying Optimality
Bibliographic record
Abstract
Autonomous systems often fuse data from multiple sensors and sensing modalities to improve robustness when operating in adverse conditions. Correct sensor fusion requires knowledge of the transformations between the sensor reference frames as well as the temporal offsets between the sensor measurement times. Due to modifications and wear-and-tear, estimates of these parameters may become inaccurate over time, and end-users of autonomous systems in turn require processes to estimate these parameters. The process of estimating the spatial transformation is known as extrinsic calibration, while the process of estimating the temporal offset is known as temporal calibration. Jointly estimating both sets of parameters is known as spatiotemporal calibration. Many state-of-the-art calibration methods require specialized targets and rough initial guesses for the parameters of interest. These requirements limit potential sensor configurations and calibration venues (e.g., the environment must contain a target and all sensors must view that singular target). In this thesis, we seek to lift these restrictions and streamline the calibration process for end-users. Initially, we explore a method to eliminate specialized targets in spatiotemporal calibration where one sensor is a radar. We then develop a targetless extrinsic calibration method for pairs of radars. Additionally, we apply recent results from convex optimization to two classic calibration problems, the hand-eye calibration problem and the hand-eye-robot-world calibration problem, yielding solutions for the optimal calibration parameters (for a dataset) without prior knowledge. Through simulation studies and real-world experiments, we demonstrate that our methods achieve estimation accuracy similar to or better than other calibration methods that require initialization or specialized targets.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".