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Marius Hofmeister

dblp:48/9806 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 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 · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 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
multi-robot mapping
0.112011
Cooperative visual mapping in a heterogeneous team of mobile robots · ICRA 2011
Robotics › Robot navigation and mapping
localization
0.012011
Cooperative visual mapping in a heterogeneous team of mobile robots · ICRA 2011
Distributed systems › distributed coordination › multi-agent systems
multi-robot coordination
0.012011
Cooperative visual mapping in a heterogeneous team of mobile robots · ICRA 2011

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

teleoperation · 0.2hierarchical team coordination · 0.2
YearPublicationVenuePosition
2011 Cooperative visual mapping in a heterogeneous team of mobile robots
abstract
Mapping is regarded as one of the most fundamental tasks for mobile robots. In this work, we present an approach that enables multiple resource-limited mobile robots to cooperatively build an image-based map of the environment and to afterwards localize in it. To achieve this, we deploy a hierarchical team of mobile robots. A parent robot possesses state-of-the-art sensors, computation power and acts as a leader. It teleoperates small child robots within its line-of-sight. In contrast to other approaches and due to the cooperation among the robots, we can relax the requirement that every robot must be able to self-localize to take part in multi-robot mapping. Additionally, our algorithm ensures the mapping of the entire area in an efficient way, i.e., it fulfills the requirements of area coverage. To test our approach, extensive experiments have been performed both in simulation and real-world. In the latter case, a team of four heterogeneous mobile robots was deployed. Besides the successful cooperation in the robot team, localization results are presented to validate the applicability of the proposed mapping procedure.
Marius Hofmeister, Marcel Kronfeld, Andreas Zell
ICRA1