Using scale-invariant feature points in visual servoing
Bibliographic record
Abstract
In this paper, we focus on the robust feature selection and investigate the application of scale-invariant feature transform (SIFT) in robotic visual servoing (RVS). We consider a camera mounted onto the endpoint of an anthropomorphic manipulator (eye-in-hand configuration). The objective of such RVS system is to control the pose of the camera so that a desired relative pose between the camera and the object of interest is maintained. It is seen that the SIFT feature point correspondences are not unique and hence those feature points with more than a unique match are disregarded. When the endpoint moves along a trajectory, the robust SIFT feature points are found and then for a similar trajectory the same selected feature points are used to keep track of the object in the current view. The point correspondences of the remaining robust feature points would provide the epipolar geometry of the two scenes, where knowing the camera calibration the motion of the camera is retrieved. The robot joint angle vector is then determined solving the inverse kinematics of the manipulator. We show how to select a set of robust features that are appropriate for the task of visual servoing. Robust SIFT feature points are scale and rotation invariant and effective when the current position of the endpoint is farther than and rotated with respect to the desired position.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".