Henning Lategahn

Papers

Total Citations

274

H-Index

About

Henning Lategahn is a computer vision and robotics researcher whose work has made significant contributions to autonomous navigation, visual localization, and scene understanding. His research focuses primarily on Visual Simultaneous Localization and Mapping (V-SLAM), visual odometry, and dynamic object detection — core challenges that underpin modern autonomous vehicle systems. Lategahn's most influential work, "Visual SLAM for Autonomous Ground Vehicles" (2011), has garnered 159 citations and remains a foundational reference in the field, addressing drift-free motion estimation through sparse landmark tracking. His follow-up research on monocular visual odometry (2018, 75 citations) tackled the notoriously difficult scale ambiguity problem inherent to single-camera systems by leveraging planar road models — a practical and elegant solution with direct real-world applications. Beyond localization, Lategahn has explored dynamic scene perception, proposing stereo-vision-based methods for detecting and tracking independently moving objects in urban environments. His development of DIRD, an illumination-robust image descriptor, reflects his attention to making visual systems reliable under challenging real-world conditions. Across his body of work, Lategahn demonstrates a consistent drive to bridge theoretical computer vision with the practical demands of autonomous robotics, earning him a respected place in the autonomous driving research community.