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About

Meiping Shi is a robotics researcher whose work focuses on the intersection of autonomous navigation, multi-robot systems, and sensor perception. Her key contributions span collaborative mapping under communication constraints, where she has developed novel multi-robot path planning algorithms that enable efficient and comprehensive map construction in large-scale outdoor environments—a critical challenge for field robotics. Shi has also made significant advances in sensor fusion, authoring a comprehensive survey on extrinsic calibration of LiDAR and camera systems, which serves as a foundational resource for researchers working on perception for autonomous vehicles. Her methodological contributions include pioneering data-driven approaches for nonlinear robot systems, such as a kernel-based Koopman operator framework for Kalman filtering that operates without requiring an explicit dynamics model. Additionally, she has explored deep reinforcement learning for dynamic target following control in autonomous driving, addressing the nonlinearities and uncertainties inherent in vehicle dynamics. With her most cited works accumulating over 25 citations, Shi’s research is establishing her as a rising voice in practical, scalable solutions for autonomous navigation and multi-agent coordination.