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J. Carbajo

dblp:82/3797 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—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 · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › robot mapping
environment modeling
0.012003
Environment modeling for topological navigation using visual landmarks and range data · ICRA 2003
Robotics › Robot navigation and mapping › mobile robot navigation › map-based navigation
topological navigation
0.012003
Environment modeling for topological navigation using visual landmarks and range data · ICRA 2003
Robotics › Robot navigation and mapping
visual landmark recognition
0.012003
Environment modeling for topological navigation using visual landmarks and range data · ICRA 2003

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

stochastic map · 0.0relaxation scheme · 0.0generalized voronoi graph · 0.0
YearPublicationVenuePosition
2003 Environment modeling for topological navigation using visual landmarks and range data
abstract
This article describes the integration in a whole navigation system of visual functions dedicated to the extraction and recognition of visual landmarks, i.e. planar quadrangles detected from a single camera. The extraction of these landmarks is based on a relaxation scheme, used to satisfy constraints between image segments. During the exploration of an indoor environment, according to the current topology, there are several ways to take advantage of these visual landmarks. Two representations are considered, relying both on laser data and or our visual landmarks system: a GVG-based model (generalized Voronoi graph) for corridors, and a composite stochastic map for open spaces. In the particular case of corridors, the corresponding modules - laser GVG construction and visual landmarks extraction and recognition - can cooperate to complete each other, as image processing can be enhanced by some structural knowledge about the scene, whereas the GVG is annotated, even as far as its edges are concerned, by qualitative visual information.
Frédéric Lerasle, J. Carbajo, Michel Devy, Jean-Bernard Hayet
ICRA2