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About

Heng Yu is a robotics researcher whose work focuses on perception, navigation, and human-robot interaction for autonomous mobile robots (AMRs). His key research areas include 3D sensing, real-time tracking, and safe navigation in human-centered environments. Yu's most cited work, "An Improved Calibration Method for a Rotating 2D LIDAR System" (2018, 48 citations), introduced a novel calibration technique for rotating 2D LIDAR systems, enabling cost-effective 3D environmental mapping—a foundational contribution for field robotics. He further advanced human-robot collaboration with an efficient human-following method that fuses kernelized correlation filters with depth information (2019, 8 citations), addressing drift issues in dynamic settings. More recently, Yu proposed a cross-layer information fusion framework for stereo matching (2021, 4 citations), targeting low-complexity, high-accuracy depth perception for autonomous driving. His latest work (2025, 3 citations) presents a safety-driven end-to-end navigation framework for AMRs that leverages sparse sensor data and human behavior prediction, tackling the dual challenge of autonomy and safety in crowded spaces. With a career spanning from foundational sensing to cutting-edge human-aware navigation, Yu's research continues to shape the future of intelligent, safe mobile robotics.