Heiner Kuhlmann
Papers
15
Total Citations
411
H-Index
9
About
Heiner Kuhlmann is a prominent researcher whose work bridges geodesy, mobile mapping, robotics, and precision agriculture, with a particular focus on 3D laser scanning, sensor fusion, and autonomous systems. His most highly cited contribution, a 2013 paper on surface feature-based classification of plant organs from 3D laser-scanned point clouds (186 citations), established a foundational framework for computational plant phenotyping — enabling automated, high-resolution analysis of crop structures that would have previously required laborious manual measurement. This work has since evolved into a broader research program exploring field robotics and high-throughput phenotyping systems, including a robotic platform capable of efficiently characterizing plant traits directly in agricultural fields. Kuhlmann's expertise extends into mobile robotics and navigation, with significant contributions to LiDAR-inertial odometry, UAV pose estimation, and simultaneous localization and mapping (SLAM). His work on GNSS-based geodetic reference frame determination further demonstrates his command of precision positioning at global scales. Across his portfolio, Kuhlmann consistently addresses challenges at the intersection of sensor technology, autonomous navigation, and real-world applicability. His research collectively reflects a commitment to advancing sustainable, data-driven agriculture and intelligent robotic systems, making meaningful contributions to both fundamental geodetic science and the practical demands of modern crop production.
Research Focus
Key Achievements
Top Papers
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- 2Fast and effective online pose estimation and mapping for UAVs41 citations · 2016
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