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About

Xingyu Chen is a robotics and artificial intelligence researcher whose work spans several interconnected domains, including LiDAR-inertial odometry, human motion prediction, robotic manipulation, and visual SLAM. His research addresses fundamental challenges in enabling intelligent mobile robots to perceive, navigate, and interact safely with dynamic real-world environments. Among his most recognized contributions is LIO-PPF, a fast LiDAR-inertial odometry system leveraging incremental plane pre-fitting and skeleton tracking, reflecting his commitment to efficient state estimation for autonomous systems. In parallel, Chen has made notable strides in human motion prediction, developing probabilistic frameworks — including Bayesian neural networks and continuous learning approaches — to move beyond deterministic models and support safer human-robot interaction. His Error Attenuation Network (EAN) further addresses the challenge of error accumulation in long-term motion forecasting. Chen has also contributed to semantic mapping in dynamic scenarios through RDS-SLAM and explored domain adaptation techniques to improve robotic grasp detection. More recently, his work on hierarchical stacking relationship prediction and distributed camera systems demonstrates a broadening focus on complex manipulation tasks. With citations accumulated across multiple research threads, Chen represents a versatile and emerging voice in intelligent robotics research.