Daniel Althoff

Papers

Total Citations

296

H-Index

About

Daniel Althoff is a leading researcher in mobile robotics and 3D perception, with a focus on advancing semantic environment understanding through robust geometry processing. His most-cited work, "Comparison of surface normal estimation methods for range sensing applications" (2009, 296 citations), provides a foundational analysis of fast algorithms for geometry segmentation and extraction—critical prerequisites for 3D object recognition in autonomous systems. This contribution has been widely adopted by the robotics community, enabling more reliable navigation and interaction in complex, unstructured environments. Althoff’s research bridges the gap between raw sensor data and high-level semantic interpretation, directly impacting the development of self-driving cars, service robots, and industrial automation. His work is distinguished by its rigorous empirical evaluation of computational methods, offering practical guidance for engineers and researchers. With over 296 citations on this paper alone, Althoff’s influence is evident in the continued reliance on his benchmarking for surface normal estimation. His achievements underscore a commitment to making robots not just mobile, but truly perceptive.