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Javier Cebolla

dblp:82/8370 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 · 44% Face, body and person analysis · 44% 3D vision · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis › human pose estimation › 2d human pose estimation
joint localization
0.112010
Explicit coordinated localization using common visual objects · ICRA 2010
Robotics › Robot navigation and mapping › localization
multi-robot localization
0.112010
Explicit coordinated localization using common visual objects · ICRA 2010

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

triangulation · 0.1stereoscopy · 0.1
YearPublicationVenuePosition
2010 Explicit coordinated localization using common visual objects
abstract
Localization in multi-robot systems is a key problem in multi agent systems. In many cases, specially involving legged robots, like the Robocup soccer competition, it relies on predefined landmarks at known locations. However, when several objects are in motion, vision occlusions due to agents in the field of view make this kind of localization unfeasible for mild time periods. This also happens if landmarks are not within the field of view. This paper presents a technique to let a robot estimate its position with respect to objects or robots by sharing whatever visual data it has with its teammates. Shared data is used to estimate the relative positions of robots watching the same object via stereoscopy. The rest of the robots can be localized via triangulation and acquire information on the position of hidden or unknown objects that other members of the team can see. The system has been tested with two Aibo ERS 7 robots from Sony and an Aiball.
Jose Manuel Peula, Javier Cebolla, Cristina Urdiales, Francisco Sandoval 0001
ICRA2