Xinglong Lei
Papers
1
Total Citations
2
H-Index
1
About
Xinglong Lei’s research focuses on intelligent mobile robotics, sensor fusion, and deep learning-based perception systems. His most cited work, “Combining Monocular Camera and 2D Lidar for Target Tracking Using Deep Convolution Neural Network based Detection and Tracking Algorithm” (2022), addresses a critical challenge in autonomous navigation: robustly detecting and tracking moving targets in dynamic environments. By fusing monocular camera imagery with 2D lidar data, Lei’s algorithm leverages the complementary strengths of each sensor—the camera’s rich visual context and the lidar’s precise spatial measurements—to improve tracking accuracy and reliability. This work is foundational for applications in service robotics, autonomous vehicles, and surveillance systems, where real-time target tracking is essential. Although early in his career, Lei’s contributions to sensor fusion and deep learning for robotics have already garnered attention, with his paper cited twice. His research continues to push the boundaries of how robots perceive and interact with their surroundings, promising significant advances in intelligent automation and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1