Youkun Deng
Papers
1
Total Citations
2
H-Index
1
About
Youkun Deng’s research centers on intelligent mobile robotics, sensor fusion, and deep learning for real-time perception. 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 systems: robustly detecting and tracking moving targets by fusing complementary sensor data. By integrating a monocular camera’s rich visual context with a 2D lidar’s precise range and angular measurements, Deng’s deep convolutional neural network framework achieves more reliable tracking than either sensor alone. This work has garnered 2 citations, laying a foundation for safer, more perceptive mobile robots in dynamic environments. Deng’s contributions are particularly valuable for applications in autonomous navigation, surveillance, and human-robot interaction, where accurate target tracking is essential. His approach exemplifies a practical, data-driven solution to sensor fusion, advancing the state of the art in intelligent robotics. For students and researchers exploring multi-modal perception, Deng’s work offers a clear, implementable pathway to enhance robotic situational awareness.
Research Focus
Key Achievements
Top Papers
- 1