Papers
6
Total Citations
24
H-Index
3
About
Stefan Hensel is a robotics researcher whose work centers on sensor fusion, simultaneous localization and mapping (SLAM), and precision measurement for autonomous systems. His major contributions include developing robust evaluation frameworks for 3D LiDAR-based SLAM algorithms, where his 2022 paper (7 citations) demonstrates how low-cost RTK GPS can validate positioning accuracy for field robotics. Hensel has also advanced computer vision through fisheye camera calibration techniques for ground-based sky imagery (2018, 6 citations), addressing critical distortion challenges that limit wider adoption. In the domain of industrial metrology, he applies deep learning to detect vibrations in digital holographic multiwavelength measurements (2023, 3 citations), enabling more reliable quality control on robotic production lines. His earlier work on Bayesian mapping with probabilistic cubic splines (2010, 3 citations) and Gaussian process estimation for magnetic field mapping (2021, 2 citations) showcases his versatility in probabilistic modeling. With over 20 citations across his portfolio, Hensel’s research bridges theoretical algorithms and practical deployment—particularly valuable for students and engineers developing autonomous systems that must operate reliably in real-world, vibration-prone environments.
Research Focus
Key Achievements
Top Papers
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- 4Bayesian mapping with probabilistic cubic splines3 citations · 2010
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- 6Application of Gaussian Process Estimation for Magnetic Field Mapping2 citations · 2021