Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing
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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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".