Chao Fan

Trine University, Chinese Academy of Sciences

Papers

Total Citations

H-Index

About

Chao Fan is a researcher working at the intersection of robotics, computer vision, and autonomous systems, with a particular focus on enabling machines to perceive and navigate complex environments intelligently. His work spans two closely related domains: simultaneous localization and mapping (SLAM) for robotic navigation, and deep learning-based depth estimation from visual data. In his 2024 paper on SLAM technology, Fan explores how robots can autonomously navigate unknown environments through real-time positioning, dynamic mapping, and path planning — a foundational challenge in modern robotics. This work has already garnered 15 citations, reflecting its relevance to the rapidly growing field of autonomous navigation. Complementing this, his 2021 contribution on joint soft-hard attention mechanisms for self-supervised monocular depth estimation addresses a critical bottleneck in affordable computer vision: extracting accurate depth information from a single camera without expensive laser sensors. Accumulating 10 citations since publication, this work demonstrates a meaningful advance in making dense depth estimation more accessible and precise. Together, Fan's research contributes to a cohesive vision of cost-effective, intelligent autonomous systems, making him a notable emerging voice in robotics and visual perception research.