Roland Jung
Papers
7
Total Citations
100
H-Index
5
About
Roland Jung is a robotics and autonomous systems researcher whose work centers on state estimation, sensor fusion, and navigation for resource-constrained aerial platforms operating in GNSS-denied environments. He has made particularly significant contributions to Radar-Inertial Odometry (RIO), developing tightly-coupled Extended Kalman Filter (EKF) frameworks that enable UAVs to accurately estimate their 6DoF pose and 3D velocity using sparse, noisy radar signals — work that has garnered nearly 50 citations and established him as a notable voice in radar-based navigation. Jung has progressively advanced these methods by incorporating techniques from the vision community, including persistent landmark tracking, and demonstrated real-time RIO deployment on embedded hardware for closed-loop UAV control. Beyond single-robot navigation, his research extends to collaborative and distributed state estimation, proposing scalable algorithms for multi-agent systems that maintain statistical optimality under communication constraints. He has also contributed to benchmarking through VINSEval, a unified evaluation framework for visual-inertial navigation algorithms. His most recent work on swarm-based localization resilience reflects a growing interest in collective robotic intelligence. Across his portfolio, Jung bridges theoretical rigor with practical deployment on real robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1Tightly-Coupled EKF-Based Radar-Inertial Odometry49 citations · 2022
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