Junhui Li

University of Science and Technology Beijing

Papers

Total Citations

H-Index

About

Junhui Li is a researcher specializing in sensor fusion and semantic mapping for autonomous systems, with a particular focus on improving environmental perception through probabilistic and evidential reasoning. Their most notable contribution is the development of SEGM (Semantic Evidential Grid Map), a novel framework introduced in their 2020 paper that fuses data from multiple sensors—such as cameras, LiDAR, and radar—into a unified, semantically rich representation of the environment. This work addresses critical challenges in autonomous navigation by combining evidence theory with deep learning, enabling more robust and interpretable scene understanding under uncertainty. While their citation count is still growing, the SEGM approach has been recognized for its potential in advancing safety-critical applications like self-driving cars and robotics. Li’s research bridges the gap between raw sensor data and high-level semantic reasoning, offering a scalable solution for real-time mapping in dynamic environments. Their work continues to influence emerging studies in multi-modal fusion and evidential AI, positioning them as a thoughtful contributor to the next generation of intelligent perception systems.