Chengjun Zhou

Nanjing University of Information Science and Technology

Papers

Total Citations

303

H-Index

About

Chengjun Zhou is a leading researcher in the field of robotic perception and autonomous navigation, with a primary focus on Simultaneous Localization and Mapping (SLAM). His work bridges the gap between traditional geometric methods and modern semantic understanding, addressing critical challenges in how robots perceive and navigate complex environments. Zhou’s most influential contribution, “An Overview on Visual SLAM: From Tradition to Semantic” (2022), has garnered 202 citations and provides a comprehensive roadmap for the field, analyzing why conventional vision-based SLAM fails in challenging conditions and how semantic information can overcome these limitations. He further advanced the discipline with “SLAM Overview: From Single Sensor to Heterogeneous Fusion” (69 citations), which systematically examines the integration of LIDAR and visual sensors for more robust localization. Most recently, Zhou has pioneered multi-robot collaborative SLAM, as detailed in his 2023 work (32 citations), proposing data fusion strategies that distribute computational load and mitigate risk in large-scale mapping tasks. His research is instrumental for students and engineers developing autonomous systems for indoor and outdoor applications, from service robots to autonomous vehicles.