Demo Data from EDBT 2026 / reviewers in the wild / expert
Marius Hofmeister
dblp:48/9806
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › robot mapping
multi-robot mapping |
0.1 | 1 | 2011 | Cooperative visual mapping in a heterogeneous team of mobile robots · ICRA 2011 |
Robotics › Robot navigation and mapping
localization |
0.0 | 1 | 2011 | Cooperative visual mapping in a heterogeneous team of mobile robots · ICRA 2011 |
Distributed systems › distributed coordination › multi-agent systems
multi-robot coordination |
0.0 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Cooperative visual mapping in a heterogeneous team of mobile robotsabstractMapping 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 |
ICRA | 1 |