Zeyong Shan

Control Vision (United States), ShanghaiTech University

Papers

Total Citations

226

H-Index

About

Zeyong Shan is a leading researcher in robotics and autonomous systems, with a primary focus on infrastructure inspection, simultaneous localization and mapping (SLAM), and multi-sensor fusion. His pioneering work on bridge maintenance robotics is exemplified by his highly cited 2011 paper on developing a crack inspection robot, which has garnered 124 citations. This research addressed critical limitations in traditional human-based bridge deck crack detection, replacing subjective visual inspection with automated, accurate robotic systems. Shan further advanced ground robot autonomy through his influential 2019 work on RGBD-Inertial trajectory estimation and mapping, cited 93 times, which demonstrated how fusing RGBD cameras with inertial sensors enables robust SLAM in challenging environments—offering a cost-effective alternative to laser-based approaches. His earlier research on data association for SLAM in robotic wireless sensor networks (2010) explored innovative techniques for joint probabilistic data association, enabling simultaneous source localization and environmental mapping. Collectively, Shan’s contributions have significantly impacted the fields of robotic inspection and autonomous navigation, providing practical solutions that enhance accuracy, reduce human error, and lower costs in critical applications such as infrastructure maintenance and ground robot operation.