Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jeff Hyams

dblp:46/5874 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1Applied, 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
2 papers
Robot navigation and mapping · 84% 3D vision · 11% Motion planning and robot control · 6%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
docking
0.012001
Low-order-complexity vision-based docking · IEEE Trans. Robotics Autom. 2001
Robotics › Robot navigation and mapping › docking
vision-based docking
0.012001
Low-order-complexity vision-based docking · IEEE Trans. Robotics Autom. 2001
Computer vision › 3D vision › multimodal perception
LiDAR-camera fusion
0.012006
People Tracking and Following with Mobile Robot using an Omnidirectional Camera and a Laser · ICRA 2006
Robotics › Robot navigation and mapping
sensor fusion
0.012006
People Tracking and Following with Mobile Robot using an Omnidirectional Camera and a Laser · ICRA 2006
Robotics › Motion planning and robot control › robot control › behavior-based control
reactive behavior
0.012001
Low-order-complexity vision-based docking · IEEE Trans. Robotics Autom. 2001

Methods — techniques the papers use, named apart from their topics

visual tracking · 0.1omnidirectional camera · 0.1laser range finder · 0.1region statistics · 0.0imprinting · 0.0color segmentation · 0.0
YearPublicationVenuePosition
2006 People Tracking and Following with Mobile Robot using an Omnidirectional Camera and a Laser
abstract
The paper presents two different methods for mobile robot tracking and following of a fast-moving person in outdoor unstructured and possibly dynamic environment. The robot is equipped with laser range-finder and omnidirectional camera. The first method is based on visual tracking only and while it works well at slow speeds and controlled conditions, its performance quickly degrades as conditions become more difficult. The second method which uses the laser and the camera in conjunction for tracking performs well in dynamic and cluttered outdoor environments as long as the target occlusions and losses are temporary. Experimental results and analysis are presented for the second approach
Marin Kobilarov, Gaurav S. Sukhatme, Jeff Hyams, Parag H. Batavia
ICRA3
2001 A Case Study of How Mobile Robot Competitions Promote Future Research
Jennifer Casper, Mark Micire, Jeff Hyams, Robin R. Murphy
RoboCup3
2001 Low-order-complexity vision-based docking
abstract
This paper reports on a reactive docking behavior which uses a vision algorithm that grows linearly with the number of image pixels. The docking robot imprints (initializes) on a two-colored docking fiducial upon departing from the dock, then uses region statistics to adapt the color segmentation in changing lighting conditions. The docking behavior was implemented on a marsupial team of robots, where a daughter micro-rover had to reenter the mother robot from an approach zone with a 2 m radius and 140/spl deg/ angular width with a tolerance of /spl plusmn/5 and /spl plusmn/2 cm. Testing during outdoor conditions (noon, dusk) and challenging indoor scenarios (flashing lights) showed that using adaptation and imprinting was more robust than using imprinting atone.
Brian W. Minten, Robin R. Murphy, Jeff Hyams, Mark Micire
IEEE Trans. Robotics Autom.3