Xiaosong Qiu
Papers
1
Total Citations
344
H-Index
1
About
Xiaosong Qiu is a leading researcher in the fields of computer vision, robotics, and deep learning, with a primary focus on simultaneous localization and mapping (SLAM) in dynamic environments. His most significant contribution is the development of Dynamic-SLAM, a pioneering semantic monocular visual localization and mapping framework that integrates deep learning to robustly handle moving objects—a critical challenge for autonomous systems. This work, published in 2019 and cited over 340 times, has become a foundational reference for researchers addressing real-world SLAM scenarios where static world assumptions fail. By leveraging semantic segmentation, Qiu’s approach enables robots and autonomous vehicles to filter out dynamic entities like pedestrians and cars, dramatically improving accuracy and reliability in cluttered settings. Beyond this landmark paper, his research spans visual odometry, object detection, and sensor fusion, consistently pushing the boundaries of perception for intelligent systems. Qiu’s work has not only advanced theoretical understanding but also provided practical solutions for applications in augmented reality, autonomous driving, and service robotics. His high citation count reflects the profound impact of his innovations, making him a key figure in the evolution of robust, real-time visual SLAM.
Research Focus
Key Achievements
Top Papers
- 1