Klaus-Uwe Gollmer
Papers
1
Total Citations
5
H-Index
1
About
Klaus-Uwe Gollmer is a researcher at the forefront of cyber-physical systems, with a particular focus on the intersection of online learning algorithms and indoor positioning technologies. His work addresses the critical challenge of enabling intelligent machines and autonomous robots to navigate and interact within complex, distributed environments. Gollmer’s most-cited paper, "Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware" (2019), exemplifies his innovative approach by demonstrating how embedded systems can leverage sound for precise indoor localization without expensive infrastructure. This contribution is pivotal for advancing the Internet of Things, where reliable, cost-effective sensing is essential. With 5 citations, this work highlights his ability to bridge theoretical machine learning with practical hardware constraints. Gollmer’s research is instrumental in creating smarter, more responsive cyber-physical systems, making him a key figure in the evolution of autonomous, networked environments.
Research Focus
Key Achievements
Top Papers
- 1