Katrina Samperi

dblp:117/4908 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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 · 67% Motion planning and robot control · 33%

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
dynamic environment mapping
0.112012
Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary · AAAI 2012
Robotics › Robot navigation and mapping › robot mapping
map representation
0.112012
Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary · AAAI 2012
Robotics › Motion planning and robot control › motion planning › sampling-based motion planning
probabilistic roadmap
0.112012
Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary · AAAI 2012

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

probabilistic roadmap · 0.1
YearPublicationVenuePosition
2012 Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary
abstract
Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.
Katrina Samperi
AAAI1