Papers
8
Total Citations
272
H-Index
4
About
Dr. Baopu Li is a leading researcher in robotics and autonomous systems, with a primary focus on path planning, computer vision, and 3D perception. His most significant contributions lie in advancing sampling-based path planning algorithms for mobile robots. He is best known for developing the **Kinematic Constrained Bi-directional RRT** (136 citations) and the **GMR-RRT*** algorithm (103 citations), which dramatically improve planning speed and solution quality in complex environments by integrating machine learning techniques like Gaussian Mixture Regression. Dr. Li also made early contributions to multi-person detection and tracking for human-robot interaction, combining HOG and LBP features within a condensation-based framework. More recently, his work has expanded into 3D deep learning, where he proposed the **Multi-view Vision Fusion Network (MvNet)** to address data-scarce learning for point cloud classification by leveraging 2D pre-trained models. His research effectively bridges the gap between classical robotics planning and modern deep learning, with his top-cited works accumulating over 240 citations, underscoring his impact on both autonomous navigation and intelligent perception systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression103 citations · 2022
- 3Condensation-based multi-person detection and tracking with HOG and LBP16 citations · 2014
- 4
- 5Learning-based Fast Path Planning in Complex Environments4 citations · 2021
- 6
- 7Particle filter based multi-pedestrian tracking by HOG and HOF2 citations · 2014
- 8