Ch. Pomrehn
Papers
1
Total Citations
8
H-Index
1
About
Ch. Pomrehn is a researcher at the forefront of autonomous systems and cooperative robotics, with a primary focus on integrating deep learning with multi-agent navigation. Their most cited work, "Deep Semantic Image Segmentation for UAV-UGV Cooperative Path Planning: A Car Park Use Case" (2020, 8 citations), tackles a critical bottleneck in unmanned ground vehicle (UGV) autonomy: the limited range of on-board sensors. By leveraging aerial imagery from unmanned aerial vehicles (UAVs), Pomrehn’s research enables UGVs to perceive and navigate unknown environments far beyond their immediate sensor horizon. This cooperative framework, which applies deep semantic segmentation to interpret complex scenes like car parks, represents a significant step toward robust, real-world deployment of heterogeneous robot teams. Pomrehn’s work has been cited in subsequent studies on multi-robot coordination and semantic mapping, underscoring its influence on the field. Their contributions are particularly notable for bridging the gap between computer vision and practical field robotics, offering a scalable solution to a persistent challenge in autonomous navigation.
Research Focus
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Top Papers
- 1