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Aroon Pahwa

dblp:73/464 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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
Robot navigation and mapping · 91% Motion planning and robot control · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › visual odometry
monocular visual odometry
0.112005
A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision · ICRA 2005
Robotics › Robot navigation and mapping
obstacle detection
0.112005
A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision · ICRA 2005
Robotics › Robot navigation and mapping
visual odometry
0.112005
A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision · ICRA 2005
Robotics › Motion planning and robot control › robot control › motion control
closed-loop motion control
0.012005
A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision · ICRA 2005

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

sparse optical flow · 0.1monocular vision · 0.1
YearPublicationVenuePosition
2005 A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision
abstract
We describe a monocular robot vision system which accomplishes accurate 3-DOF dead-reckoning, closed loop motion control, and precipice and obstacle detection, all in dynamic environments, using a single, consumer-grade web cam and typical laptop computer hardware. Simultaneous translation and rotation are accurately measured, and the camera need not be placed at the robot’s center of rotation. The algorithm is straightforward to implement and robust to noisy measurements. The software is based on open source computer vision libraries and is itself open source. It has been tested in a wide variety of real-world environments and on several different mobile robot platforms.
Jason Campbell, Rahul Sukthankar, Illah R. Nourbakhsh, Aroon Pahwa
ICRA4