Demo Data from SIGIR 2026 / reviewers in the wild / expert
Uday S. Pathre
dblp:129/9019
· 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 |
3D vision · 36% Trustworthy machine learning · 28% Motion planning and robot control · 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 | Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991 |
Robotics › Motion planning and robot control
robot calibration |
0.0 | 1 | 1991 | Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991 |
Computer vision › 3D vision
vision-based measurement |
0.0 | 1 | 1991 | Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991 |
Computer vision › 3D vision
camera calibration |
0.0 | 1 | 1991 | Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991 |
Robotics › Robot manipulation
parameter identification |
0.0 | 1 | 1991 | Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991 |
Methods — techniques the papers use, named apart from their topics
vision-based theodolite · 0.0nonlinear regression · 0.0coordinate measuring machine · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Vision-based automatic theodolite for robot calibrationabstractAn automatic partial-pose measurement system for robot calibration, called the vision-based automatic theodolite (VBAT), is described. It uses low-resolution rotation stages and resolution enhancement from a vision system to determine the line of sight to a spherical illuminated target. Automatic tracking, focusing, and centering provide the calibration system with speed, reliability, and repeatability. It is proposed that such a measurement prototype may be constructed without expensive precision machining, provided it is calibrated before use. Simulations demonstrate that such a system may be identified by precalibration of the vision parameters, followed by calibration of the rotation stage parameters. All parameters are determined from measurements using a coordinate measuring machine. The parameter identification is formulated as a nonlinear regression problem. Verification of the VBAT calibration experiment shows that the identified model can predict the target position to a root mean square miss-distance of 0.2 mm.> Morris Driels, Uday S. Pathre |
IEEE Trans. Robotics Autom. | 2 |