Demo Data from EDBT 2026 / reviewers in the wild / expert
Raymond Hanus
dblp:52/10665
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Motion planning and robot control · 50% 3D vision · 25% Optimization for machine learning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Optimization for machine learning
error compensation |
0.0 | 1 | 1991 | Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991 |
Computer vision › 3D vision › geometric estimation
geometric parameter estimation |
0.0 | 1 | 1991 | Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991 |
Robotics › Motion planning and robot control › robot calibration
kinematic calibration |
0.0 | 1 | 1991 | Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991 |
Robotics › Motion planning and robot control › robot control › motion control
robot positioning |
0.0 | 1 | 1991 | Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Kinematic calibration and geometrical parameter identification for robotsabstractA technique is presented for the calibration of robots based on a maximum-likelihood approach for the identification of geometrical errors. A new experimental setup is presented for measurement of the end-reflector position errors. The errors of position and orientation of the measuring device are included in the algorithm and identified. Tests have been carried out on a robot with six degrees of freedom. Tests show that this technique can reduce the mean error distance by a factor of more than 15. Compensation algorithms are presented, based on the improved knowledge of the geometrical model.> Jean-Michel Renders, Eric Rossignol, Marc Becquet, Raymond Hanus |
IEEE Trans. Robotics Autom. | 4 |