Yingke Chen
Papers
1
Total Citations
32
H-Index
1
About
Yingke Chen is a pioneering researcher in bio-inspired robotics and autonomous navigation, whose work bridges computational neuroscience and practical SLAM (simultaneous localization and mapping) systems. Chen’s most notable contribution is the development of a brain-inspired SLAM system based on ORB features, published in 2017, which has garnered 32 citations. This innovative approach integrates RatSLAM—a neural model of rodent spatial cognition—with robust visual feature extraction, enabling mobile robots to navigate complex environments using RGB sensors alone. By mimicking the hippocampal place cells and grid cells found in mammalian brains, Chen’s system achieves efficient, biologically plausible mapping without relying on expensive LiDAR or depth sensors. The work stands out for its elegant fusion of neurobiology and computer vision, offering a lightweight, scalable solution for autonomous robotics. Chen’s research has significant implications for field robotics, where power and cost constraints demand efficient algorithms. With a growing citation footprint, Chen continues to influence the intersection of neuromorphic computing and real-world robotic applications, inspiring new generations of researchers to look to nature for engineering solutions.
Research Focus
Key Achievements
Top Papers
- 1A brain-inspired SLAM system based on ORB features32 citations · 2017