Christof Eberst
Papers
8
Total Citations
79
H-Index
5
About
Christof Eberst’s research lies at the intersection of autonomous mobile robotics, computer vision, and industrial automation, with a particular focus on enabling machines to perceive, navigate, and interact with their environments. His most influential work, “Vision-based door-traversal for autonomous mobile robots” (24 citations), established a robust, low-cost method for robots to recognize and safely navigate through doors using monocular grey-level images—a foundational capability for indoor mobile robotics. Eberst further advanced environmental understanding through his work on vision-based model generation for indoor spaces (19 citations), where he developed techniques to extract 3D descriptions from video camera data using contour tracing. In the industrial domain, his paper on sensor-based robotics for bore inspection (15 citations) addressed a critical manufacturing challenge: automating the high-precision inspection of internal threads and bores in low-volume, high-variant parts. Eberst also contributed to cognitive vision systems, exploring how redundant visual cues can be integrated for robust object recognition. His work on programming robots by gestures (2 citations) foreshadowed modern human-robot collaboration trends. Though his citation counts are modest, Eberst’s contributions are notable for their practical, systems-level approach to bridging perception and action in real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1Vision-based door-traversal for autonomous mobile robots24 citations · 2002
- 2Vision based model generation for indoor environments19 citations · 2002
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