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Uday S. Pathre

dblp:129/9019 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
calibration
0.011991
Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control
robot calibration
0.011991
Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991
Computer vision › 3D vision
vision-based measurement
0.011991
Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991
Computer vision › 3D vision
camera calibration
0.011991
Vision-based automatic theodolite for robot calibration · IEEE Trans. Robotics Autom. 1991
Robotics › Robot manipulation
parameter identification
0.011991
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
YearPublicationVenuePosition
1991 Vision-based automatic theodolite for robot calibration
abstract
An 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