Frank Neuhaus
Papers
7
Total Citations
128
H-Index
4
About
Frank Neuhaus is a leading researcher in autonomous robotics, specializing in perception and navigation for unstructured environments. His work centers on terrain classification, drivability analysis, and 3D mapping, enabling robots to operate safely in challenging, real-world settings like disaster zones. Neuhaus’s most influential contribution is the development of probabilistic methods for classifying terrain from 3D laser range data, allowing autonomous systems to distinguish drivable from hazardous surfaces in real time. His 2012 paper on probabilistic terrain classification (41 citations) and his 2009 study on drivability analysis (39 citations) are foundational in the field, demonstrating how geometric data can be transformed into actionable navigation cues. A notable achievement is his 2010 field report from Disaster City, which validated these algorithms in rough, post-disaster environments—a critical step beyond pristine laboratory tests. Neuhaus has also advanced large-scale mapping with Markov random fields and integrated 2D/3D perception for robust SLAM, as seen in his GraphSLAM system. His work has directly influenced rescue robotics and autonomous exploration, with his papers collectively cited over 120 times, underscoring his impact on practical, terrain-aware navigation.
Research Focus
Key Achievements
Top Papers
- 1Probabilistic terrain classification in unstructured environments41 citations · 2012
- 2
- 3Real-time 3D mapping of rough terrain: A field report from Disaster City29 citations · 2010
- 4Markov random field terrain classification of large-scale 3D maps8 citations · 2014
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- 7High-resolution hyperspectral ground mapping for robotic vision3 citations · 2018