Yan Ding

Papers

Total Citations

H-Index

About

Yan Ding is an emerging researcher in the field of simultaneous localization and mapping (SLAM) and computer vision, with a particular focus on robust perception in dynamic environments. His most notable work, DOC-SLAM (Dynamic Object Culling SLAM), introduced in 2021, addresses one of the persistent challenges in autonomous navigation and robotics: accurately estimating camera trajectories when scenes contain moving objects. By integrating semantic information with stereo vision, DOC-SLAM demonstrates strong performance in highly dynamic environments where traditional SLAM systems often fail. This contribution has already attracted 7 citations, reflecting growing interest from the robotics and computer vision communities in solutions that bridge semantic understanding with geometric estimation. Ding's research sits at a critical intersection of deep learning and classical SLAM pipelines, tackling real-world deployment challenges that matter greatly for autonomous vehicles, mobile robotics, and augmented reality applications. His work represents a meaningful step forward in making SLAM systems more reliable and practically viable, and positions him as a promising contributor to the ongoing evolution of intelligent, environment-aware robotic systems.