Rudolph Triebel
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), University of Freiburg, Carnegie Mellon University, Technical University of Munich, ETH Zurich, École Polytechnique Fédérale de Lausanne, Board of the Swiss Federal Institutes of Technology, University of Oxford, Oxford Research Group, SystemsX.ch
Papers
82
Total Citations
4,006
H-Index
25
About
Rudolph Triebel is a prominent researcher whose work spans robotics, autonomous systems, and machine learning, with particular expertise in probabilistic mapping, environment perception, and uncertainty quantification in deep learning. His foundational contributions to mobile robotics include pioneering methods for outdoor terrain mapping using multi-level surface representations and volumetric mapping of complex environments such as underground mines, work that helped establish robust frameworks for robot localization and navigation in challenging, real-world settings. His research into dynamic environment mapping and 3D scan classification further advanced the field of autonomous perception. Triebel's later work broadened into socially aware robotics, exemplified by the SPENCER project — a service robot designed to guide passengers in busy airports — and heterogeneous multi-robot teams for planetary exploration through the ARCHES mission. Perhaps his most widely recognized contribution is a comprehensive survey on uncertainty in deep neural networks (2023), which has accumulated over 1,100 citations and has become an essential reference for researchers grappling with model reliability and trustworthiness in AI systems. With a body of work spanning foundational robotics to cutting-edge deep learning, Triebel has demonstrated sustained impact across multiple disciplines, making his research essential reading for anyone working at the intersection of autonomous systems and machine learning.
Research Focus
Key Achievements
Top Papers
- 1A survey of uncertainty in deep neural networks1,134 citations · 2023
- 2Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing346 citations · 2006
- 3Map building with mobile robots in dynamic environments270 citations · 2004
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- 5A system for volumetric robotic mapping of abandoned mines257 citations · 2004
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- 8A Layered Approach to People Detection in 3D Range Data132 citations · 2010
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- 10Robust 3D scan point classification using associative Markov networks84 citations · 2006