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About

Yonggen Ling is a robotics researcher whose work spans the full spectrum of autonomous systems, with particular expertise in visual perception, simultaneous localization and mapping (SLAM), and human-robot interaction. His most influential contributions address fundamental challenges in making robots perceive and navigate complex real-world environments. His 2016 work on markerless stereo extrinsic calibration (31 citations) tackled a critical bottleneck in depth estimation accuracy, while his 2017 paper on building navigation maps from sparse SLAM features (26 citations) helped bridge the persistent gap between theoretical mapping algorithms and practical autonomous navigation. Ling has further advanced robot embodiment through research in real-time dense mapping, quadruped locomotion on challenging terrains, and multi-fingered tactile servoing for robust grasping under uncertainty. His attention-oriented action recognition framework for human-robot interaction demonstrates a commitment to making robots responsive to human behavior in dynamic settings. More recently, Ling has pushed into cutting-edge territory with category-level object reconstruction from stereo imagery and zero-shot vision-language navigation, reflecting his ambition to integrate language-grounded reasoning into autonomous systems. Collectively, his work represents a coherent and forward-looking research agenda in intelligent mobile and interactive robotics.