Michael Barjenbruch

Papers

Total Citations

184

H-Index

About

Michael Barjenbruch is a leading researcher in autonomous vehicle perception and mobile robotics, with a primary focus on radar-based ego-motion estimation. His major contributions center on developing robust, real-time algorithms that allow vehicles and robots to determine their own motion—longitudinal and lateral velocity—using Doppler radar sensors, a critical capability for advanced driver-assistance systems (ADAS) and localization. His most influential work, "Instantaneous ego-motion estimation using multiple Doppler radars" (132 citations), introduced a robust method for instantly computing a vehicle’s 2D motion state, directly impacting the reliability of autonomous navigation. Barjenbruch further advanced the field with a fast probabilistic framework that integrates spatial and Doppler velocity data, leveraging the Normal Distribution Transform (NDT) for efficient radar scan registration. He also explored multi-robot grid map alignment, enabling parallel environmental exploration. With over 180 total citations across his key papers, Barjenbruch’s research has been foundational for radar-based perception, offering practical, high-speed solutions that bridge the gap between sensor data and real-time vehicle control. His work remains essential reading for engineers developing safe, self-localizing autonomous systems.