Papers
4
Total Citations
66
H-Index
3
About
Norbert Strobel’s research lies at the intersection of robotics, computer vision, and machine learning, with a focus on enabling autonomous systems to perceive and navigate complex environments. His most cited work, “People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot” (2021, 48 citations), introduces a novel approach that combines depth-based silhouette extraction with CNNs, significantly improving human detection accuracy for mobile robots in dynamic settings. This contribution is particularly valuable for safe human-robot interaction in industrial and service robotics. Strobel also advanced medical robotics through “A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model” (2018, 9 citations), demonstrating how surface data can predict internal anatomical features—a key step toward non-invasive surgical guidance. His survey on RGB-D indoor robot navigation methods (2020, 7 citations) provides a comprehensive experimental comparison of ROS-based techniques, integrating wheel odometry and IMU data to enhance localization robustness. Additionally, his benchmark for mobile robot localization in challenging industrial environments (2021) offers a standardized evaluation framework for multi-sensor systems. Strobel’s work consistently bridges perception and navigation, making tangible impacts on both robotics research and real-world deployment.
Research Focus
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Top Papers
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