Stephan Manthe
Papers
2
Total Citations
23
H-Index
2
About
Stephan Manthe is a robotics researcher whose work centers on autonomous navigation, visual perception, and semantic understanding for aerial and service robots. His key contributions lie in developing robust localization methods that fuse stereo visual odometry with semantic information, enabling mini-aerial robots to reliably operate in GPS-denied indoor environments. In his most-cited work, "Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments" (2018, 16 citations), Manthe proposed a novel particle filter approach that leverages both geometric and semantic cues to significantly improve pose estimation accuracy. He also played a pivotal role in Team Homer@UniKoblenz, contributing to the RoboCup@Home competition through a layered software architecture that streamlined rapid application development for domestic service robots. His research bridges the gap between low-level visual odometry and high-level semantic reasoning, advancing the practical deployment of autonomous robots in complex, human-centric spaces. With a focus on real-world robustness and competition-driven innovation, Manthe’s work continues to influence the fields of aerial robotics and indoor autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2