Daniel Gehrig

University of Zurich, Robotics Research (United States)

Papers

Total Citations

161

H-Index

About

Daniel Gehrig is a prominent researcher specializing in event-based vision and autonomous robotics, with a particular focus on developing perception systems that push the boundaries of what robots can sense and do in challenging environments. His work centers on event cameras — novel neuromorphic sensors that offer extraordinary resilience to motion blur and excel in low-light and high dynamic range conditions — and their integration into real-world robotic systems. Gehrig's most influential contribution, "Exploring Event Camera-Based Odometry for Planetary Robots" (2022, 69 citations), demonstrates the transformative potential of event cameras for future Mars helicopter missions, addressing critical limitations in existing visual-inertial odometry algorithms. His work on unsupervised domain adaptation (49 citations) has been instrumental in bridging the gap between event-based and conventional frame-based vision, helping overcome a key bottleneck that has historically slowed progress in the field. Beyond aerial applications, Gehrig has extended event-based perception to quadrupedal robotics, enabling agile object catching in dynamic scenarios (28 citations), and has more recently developed deep learning approaches for visual odometry combining events and frames. With over 160 cumulative citations across his key works, Gehrig's research is shaping next-generation robotic perception, making autonomous systems more reliable, faster, and capable across terrestrial and extraterrestrial environments alike.