Shunping Ji

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About

Shunping Ji is a leading researcher in the fields of autonomous navigation, 3D mapping, and robotic perception. His most impactful work centers on advancing Simultaneous Localization and Mapping (SLAM) systems, particularly by integrating multi-sensor data to achieve robust and accurate localization in complex outdoor environments. A key contribution is his pioneering development of a GPS-supported visual SLAM framework using a rigorous sensor model for panoramic cameras, a foundational paper that has garnered 42 citations. Ji has further pushed the boundaries of sensor fusion by designing a panoramic visual-inertial SLAM system tightly coupled with a wheel encoder, achieving 23 citations for its practical applicability in mobile mapping and driverless cars. Beyond localization, he has made significant strides in scene understanding, notably with his Multi-Scale Attentive Aggregation Network (MSAAN) for LiDAR point cloud segmentation, which has earned 32 citations for its innovative approach to achieving global feature consistency. Through these contributions, Ji has established himself as a key figure in developing the robust, multi-modal perception systems essential for the next generation of autonomous robots and vehicles.