Papers
22
Total Citations
503
H-Index
9
About
Jingdao Chen is a researcher specializing in robotics, autonomous navigation, and 3D sensing technologies, with a particular focus on applying these systems to construction and disaster-response environments. His most influential work centers on Simultaneous Localization and Mapping (SLAM), where his 2018 paper "SLAM-driven robotic mapping and registration of 3D point clouds" has garnered an impressive 243 citations, establishing him as a leading voice in mobile robot perception. Chen has made significant contributions to real-time 3D point cloud processing, developing methods for semantic segmentation, object recognition from thermal-mapped data, and incremental multi-view mapping that enable robots to intelligently interpret complex environments. His research extends into construction site automation, where he has pioneered autonomous robot localization in unknown environments to improve productivity and safety. More recently, Chen has broadened his scope to address security vulnerabilities in AI-robotics systems, reflecting a timely awareness of emerging challenges in the field. Across his body of work, he has demonstrated a consistent ability to bridge foundational robotics research with real-world applications, making his contributions valuable to students and practitioners in robotics, computer vision, and smart infrastructure alike.
Research Focus
Key Achievements
Top Papers
- 1SLAM-driven robotic mapping and registration of 3D point clouds243 citations · 2018
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- 4Robotic sensing and object recognition from thermal-mapped point clouds27 citations · 2017
- 5Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots26 citations · 2019
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- 7Real-time 3D Mobile Mapping for the Built Environment15 citations · 2016
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- 10