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About

Risheng Kang is a pioneering researcher at the intersection of robotics, computer vision, and non-destructive testing, whose work is reshaping how autonomous systems perceive and interact with their environments. His primary research areas include simultaneous localization and mapping (SLAM) in dynamic environments, robotic computed tomography (CT) imaging, and geometric calibration for flexible robotic systems. Kang’s most impactful contribution is his work on MISD‐SLAM, a multimodal semantic SLAM framework that addresses the long-standing challenge of operating in dynamic, high-level semantic scenes—a critical step toward truly autonomous mobile robots. This paper has garnered 25 citations, reflecting its significance in the field. In the domain of robot-CT, Kang has made substantial advances in geometric calibration and trajectory optimization, developing reference-free methods for imaging geometry estimation and investigating how robot properties affect CT system performance. His work on geometric qualification for flexible trajectories, with 7 citations, demonstrates his ability to solve practical engineering problems. Kang’s research bridges the gap between theoretical SLAM algorithms and real-world industrial applications, making him a notable figure in both robotics and computed tomography communities.