Dalin Li

Papers

Total Citations

H-Index

About

Dalin Li is a researcher whose work lies at the intersection of robotics, intelligent environments, and human-robot interaction, with a particular focus on people detection and perception using laser-based sensors. His most cited paper, "A Multi-Type Features Method for Leg Detection in 2-D Laser Range Data" (2017, 26 citations), addresses a critical challenge in real-world robotics: reliably detecting human legs in cluttered, dynamic environments where legs may be touching or partially occluded. Li’s key contribution is the development of a multi-type feature extraction method that combines geometric, statistical, and motion-based descriptors to improve detection accuracy and robustness over traditional single-feature approaches. This work has practical implications for security surveillance, autonomous navigation, and assistive robotics, enabling safer and more responsive human-aware systems. While his citation count reflects a focused, emerging impact, Li’s research is notable for tackling a fundamental sensing problem that underpins many advanced applications. His method offers a practical, computationally efficient solution for leg detection in real-time systems, making it a valuable reference for researchers working on laser-based people tracking and human-robot coexistence.