Feng Jie

Papers

Total Citations

H-Index

About

Feng Jie is a robotics researcher whose work centers on visual simultaneous localization and mapping (SLAM), with a particular focus on making these systems robust in dynamic, real-world environments. Their major contribution lies in addressing a fundamental limitation of traditional SLAM algorithms, which typically assume a static world—an assumption that breaks down in the presence of moving objects like people or vehicles. In their highly cited 2025 paper, "A Visual SLAM System in Dynamic Environments Based on ORB-SLAM3," Feng Jie proposes a novel framework that extends the state-of-the-art ORB-SLAM3 to handle dynamic scenes by filtering out feature points on moving objects. This work is critical for advancing autonomous navigation in crowded or unpredictable settings, such as service robots in homes or autonomous vehicles in traffic. With 1 citation already in a short time, the paper signals growing recognition of its practical importance. By tackling the static-world bottleneck, Feng Jie is helping to bridge the gap between laboratory SLAM and deployment in the messy, dynamic world where robots must actually operate.