Rongjun Ding
Papers
3
Total Citations
13
H-Index
2
About
Rongjun Ding is a researcher at the forefront of autonomous systems and robotics, with key contributions spanning sensor fusion, simultaneous localization and mapping (SLAM), and pedestrian behavior prediction. His work addresses critical challenges in enabling reliable perception and navigation for autonomous vehicles and mobile robots. Ding’s notable contributions include the development of a target-free self-calibration method for stereo camera-GNSS/IMU systems, which eliminates the need for specific vehicle maneuvers, achieving 6 citations since 2023. He also advanced LiDAR-based SLAM with the WiCRF2 framework, which enhances localization accuracy through multi-weighted feature extraction and motion observability analysis, garnering 5 citations. More recently, Ding has ventured into pedestrian trajectory prediction, introducing a physical-guided position association learning approach that improves prediction robustness in complex environments. His research is characterized by a practical focus on real-world deployment, addressing sensor fusion, motion estimation, and safety-critical prediction. With a growing citation impact and a trajectory of innovative work, Rongjun Ding is shaping the future of autonomous navigation and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3