Yidan Long

Papers

Total Citations

H-Index

About

Yidan Long is an emerging researcher specializing in computer vision and autonomous systems, with a particular focus on Simultaneous Localization and Mapping (SLAM) in complex real-world environments. Their most recognized contribution, "Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes" (2023), has garnered significant attention — accumulating over 56 citations across multiple venues — and addresses one of the field's most persistent challenges: the assumption of static environments in conventional SLAM algorithms. By developing a visual SLAM framework capable of handling dynamic outdoor scenes, Long's work directly advances the reliability and practicality of autonomous navigation systems in unpredictable, real-world conditions. This research has meaningful implications across a broad spectrum of applications, including augmented and virtual reality (AR/VR), robotics, and self-driving vehicles — technologies that demand robust environmental perception. Long's ability to tackle fundamental limitations in existing SLAM methodologies positions them as a promising contributor to the robotics and computer vision communities. Students and researchers working on autonomous systems, mobile robotics, or scene understanding will find Long's work a valuable reference point for bridging theoretical SLAM frameworks with dynamic, real-world deployment challenges.