Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing
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
Robust and real-time relative pose estimation is an integral part of a position-based visual servoing (PBVS) system. Traditionally, extended Kalman filter (EKF) has been used to solve for the nonlinear relative end-effector to object pose equations from a set of 2D-3D point correspondences. However, the performance of the estimation filter and the convergence of the pose estimates are highly sensitive to tuning of filter parameters, camera calibration, and image processing. In this paper, the application of iterated EKF (IEKF) for a robust high-speed PBVS system is studied. We also provide a detailed analysis of the stability and sensitivity of the EKF and IEKF pose estimation to uncertainties in (1) tuning of filter parameters, namely, process and measurement noise covariance matrices, initial state estimate, and sampling time (speed of PBVS system), (2) features selection, and (3) calibration of camera intrinsic parameters. Experimental results show that IEKF outperforms the standard EKF without bandwidth sacrifice and should be used to improve the robustness of the PBVS system to uncertainties
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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