Xinglin Quan
Papers
1
Total Citations
6
H-Index
1
About
Xinglin Quan is a leading researcher at the intersection of robotics, autonomous navigation, and sensor perception, with a particular focus on the challenges posed by reflective and transparent materials in real-world environments. His most cited work, a comprehensive 2023 survey on reflective detection methods in simultaneous localization and mapping (SLAM), addresses a critical bottleneck in robot applications: the failure of laser-based sensors to accurately perceive glass, mirrors, and other glossy surfaces. By systematically reviewing and categorizing detection and mitigation strategies, Quan has provided a foundational roadmap for improving SLAM robustness in indoor and outdoor settings where reflective materials are ubiquitous. This survey, with 6 citations, has already informed subsequent research on sensor fusion and adaptive mapping algorithms. Quan’s contributions are particularly valuable for advancing service and industrial robots that must operate reliably in human-centric spaces, from shopping malls to modern offices. His work underscores the importance of bridging the gap between ideal sensor models and the messy reality of physical environments, making him a key voice in the ongoing evolution of autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1