Demo Data from SIGIR 2026 / reviewers in the wild / expert
B. Preising
dblp:153/6604
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 · 44% Trustworthy machine learning · 28% 3D vision · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
calibration |
0.0 | 1 | 1991 | Robot performance measurement and calibration using a 3D computer vision system · ICRA 1991 |
Robotics › Motion planning and robot control
robot calibration |
0.0 | 1 | 1991 | Robot performance measurement and calibration using a 3D computer vision system · ICRA 1991 |
Computer vision › 3D vision
vision-based measurement |
0.0 | 1 | 1991 | Robot performance measurement and calibration using a 3D computer vision system · ICRA 1991 |
Robotics › Motion planning and robot control
robot control |
0.0 | 1 | 1991 | Robot performance measurement and calibration using a 3D computer vision system · ICRA 1991 |
Robotics › Motion planning and robot control › robot control › sensor-based control
vision-based control |
0.0 | 1 | 1991 | Robot performance measurement and calibration using a 3D computer vision system · ICRA 1991 |
Methods — techniques the papers use, named apart from their topics
vision feedback · 0.0kinematic equations · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Robot performance measurement and calibration using a 3D computer vision systemabstractThe use of a single-camera 3D computer vision system as a position sensor in order to perform robot calibration is examined. A vision feedback scheme, termed vision-guided robot control (VRC), that can improve the accuracy of a robot in an online iterative manner is described. This system demonstrates the advantage that can be achieved by a Cartesian space robot control scheme when end-effector position and orientation are actually sensed instead of calculated from the kinematic equations. The viability of using a vision system for robot calibration is demonstrated by experimentally showing that the accuracy of a robot can be drastically improved. The vision system can also be used to determine the repeatability and accuracy of a robot in a simple, efficient, and quick manner. Experimental work with an IBM electric drive robot and the proposed vision system produced a 97- and 145-fold improvement in the position and orientation accuracy of the robot, respectively.> B. Preising, Tien C. Hsia |
ICRA | 1 |