Papers

8

Total Citations

51

H-Index

4

About

Juho Vihonen is a Finnish researcher whose work centers on robotics, sensor fusion, and motion estimation, with particular expertise in MEMS-based inertial sensing systems for multi-body robotic manipulators. His most influential contributions focus on leveraging low-cost microelectromechanical systems (MEMS) rate gyros and linear accelerometers to achieve precise, lag-free motion state estimation in rigid open-chain linkage assemblies — a technically demanding problem with significant implications for industrial robotics and automation. His 2013 paper on geometry-aided angular acceleration sensing stands as his most cited work, accumulating 20 citations, and established a foundation for subsequent research into low-noise angular velocity and acceleration estimation across serial link manipulators. Beyond inertial sensing, Vihonen has demonstrated a broader interest in robotic perception and security systems. His earlier work on classifying metallic objects in walk-through metal detectors using polarizability tensor eigenvalues reflects creative cross-disciplinary thinking. More recently, he has extended his research into vision-based mobile robot control, stereo-vision pose estimation, and flexible-link deformation modeling, addressing real-world challenges in latency, noise, and structural compliance. Across his career, Vihonen has consistently pursued practical, hardware-grounded solutions to complex robotic sensing challenges, making him a notable contributor to the field of applied robotics and embedded sensing.

Research Focus

Key Achievements

4
H-Index
8
Papers
51
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
Geometry-aided angular acceleration sensing of rigid multi-body manipulator using MEMS rate gyros and linear accelerometers
20 citations · 2013
📈 Most Prolific Year: 2017 (3 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: Tampere University of Applied Sciences, Tampere University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago