Papers
1
Total Citations
16
H-Index
1
About
Yihuai Wu has made significant contributions to intelligent robotics and autonomous navigation, with a focus on sensor fusion and deep learning for obstacle avoidance. Their most-cited work, "Design and implementation of a novel obstacle avoidance scheme based on combination of CNN-based deep learning method and liDAR-based image processing approach" (2018, 16 citations), introduced a pioneering hybrid framework that integrates a 10-layer Convolutional Neural Network with LiDAR-based image processing. This approach addressed the limitations of single-sensor or single-algorithm systems by combining the perceptual strengths of deep learning with the spatial accuracy of LiDAR, enabling more robust and adaptive obstacle avoidance in dynamic environments. Wu's research bridges the gap between computer vision and robotics, offering a scalable solution for autonomous vehicles and mobile robots. Their work has been cited in studies advancing sensor fusion techniques and real-time navigation systems. By demonstrating how deep learning can complement traditional sensor processing, Wu has helped pave the way for safer, more intelligent autonomous systems. Their contributions continue to influence researchers exploring multi-modal perception and adaptive control in robotics.
Research Focus
Key Achievements
Top Papers
- 1