Feng Li

Guangdong University of Technology, Munich Center for Machine Learning

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About

Feng Li is a pioneering researcher at the intersection of robotics, medical imaging, and autonomous navigation. His work centers on two transformative areas: enhancing robotic perception through semantic visual SLAM, and advancing multi-modality medical imaging systems for safer, more precise interventions. Li’s most impactful contribution is his 2020 paper on a mobile robot visual SLAM system with enhanced semantics segmentation, which has garnered 32 citations. This work addresses a critical limitation of traditional SLAM systems—their inability to perform robustly in large-scale, dynamic environments. By integrating semantic information, Li’s approach enables robots to understand and navigate complex, changing spaces, a breakthrough with profound implications for autonomous robotics. In the medical domain, Li is shaping the future of image-guided procedures. His 2025 paper on robotic CBCT (cone-beam computed tomography) combined with robotic ultrasound, with 6 citations, tackles the challenge of limited dexterity and mobility in current imaging devices. This work promises to deliver optimal fused images for interventions like needle insertion. Additionally, his concept of the Intelligent Virtual Sonographer (IVS) aims to revolutionize physician-robot-patient communication. Through these achievements, Li is bridging the gap between robotic perception and clinical practice, driving innovations that enhance both autonomy and patient care.