Papers
4
Total Citations
10
H-Index
2
About
Zhendong Fan is an emerging researcher specializing in robotics, simultaneous localization and mapping (SLAM), and hardware-accelerated computing for autonomous mobile robots. His work sits at the intersection of algorithmic innovation and embedded systems design, with a particular focus on making robot navigation more robust and computationally efficient in real-world environments. Fan's most notable contributions center on impact-aware scan matching, addressing a critical vulnerability in conventional correlative scan matching (CSM) algorithms when robots encounter physical disturbances — a common challenge in human-populated environments. His RIA-CSM and RIA-CSM2 frameworks (2022, 2024) introduced real-time solutions leveraging heterogeneous multi-core System-on-Chip (SoC) architectures, earning a combined seven citations and establishing him as a thoughtful problem-solver in low-cost wheeled robot applications. Beyond scan matching, Fan has pioneered energy-efficient SLAM accelerator hardware, developing reconfigurable EKF-SLAM processors deployed on FPGA-based SoC platforms. These systems achieve high frame rates while minimizing power consumption — qualities essential for practical edge robotics deployment. Though early in his career, Fan's consistent focus on bridging algorithmic rigor with hardware practicality positions him as a promising contributor to the autonomous robotics community, with growing recognition reflected in his expanding citation record.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4