Michael Kusenbach

Universität der Bundeswehr München, University of Koblenz and Landau

Papers

Total Citations

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About

Michael Kusenbach is a researcher whose work lies at the intersection of autonomous navigation and perception, with a particular focus on enabling robots to understand and move through large-scale outdoor environments using human-intuitive cues. His most significant contribution is a landmark-based mapping and navigation system that leverages vision and LiDAR data to detect road segments, intersections, and salient structures like houses and trees. By representing these features in a compact, sparse metric-topological map, Kusenbach’s approach allows robots to navigate using human-recognizable landmarks, bridging the gap between machine perception and natural human spatial reasoning. This work, published in 2015, has garnered 13 citations, reflecting its relevance in the field of field robotics. Additionally, Kusenbach has explored person detection in depth data using Fourier features, a technique that enhances robotic awareness of human presence in complex environments. His research is notable for its practical, real-world applicability, offering a pathway toward more intuitive and robust autonomous systems that can operate reliably in unstructured outdoor settings.