Zaiyu Peng
Papers
1
Total Citations
2
H-Index
1
About
Zaiyu Peng is a researcher focused on intelligent mobile robotics and sensor fusion, with particular expertise in moving target detection and tracking. His work integrates monocular cameras with 2D LiDAR systems, leveraging deep convolutional neural networks to enhance real-time object detection and tracking capabilities. In his most-cited paper, "Combining Monocular Camera and 2D Lidar for Target Tracking Using Deep Convolution Neural Network based Detection and Tracking Algorithm" (2022), Peng addresses a fundamental challenge in robotics: the trade-off between precise spatial localization from LiDAR and the rich visual context from cameras. By fusing these sensor modalities, his approach improves tracking accuracy in dynamic environments, a critical requirement for autonomous navigation and human-robot interaction. Though early in his career with 2 citations on this work, Peng's research contributes to the growing field of multi-sensor perception systems. His methodology has potential applications in service robots, autonomous vehicles, and industrial automation, where reliable target tracking under varying conditions remains a key obstacle. Peng's work represents an important step toward more robust and efficient perception pipelines for intelligent mobile systems.
Research Focus
Key Achievements
Top Papers
- 1