Dengke Xu
Papers
1
Total Citations
1
H-Index
1
About
Dengke Xu is a leading researcher in energy-efficient embedded systems for autonomous robotics, with a focus on real-time localization and mapping. His most cited work, “An Energy-Efficient, High-Frame-Rate, and Reconfigurable EKF-SLAM Processor With Full Acceleration for Autonomous Mobile Robots” (2025), addresses a critical bottleneck in intelligent edge applications: achieving efficient, high-speed simultaneous localization and mapping (SLAM) on resource-constrained platforms. Xu’s major contribution lies in designing a fully accelerated, reconfigurable processor architecture for the Extended Kalman Filter (EKF)-SLAM algorithm, enabling autonomous mobile robots (AMRs) to operate with unprecedented energy efficiency and frame rates. This work has already garnered early citations, signaling its impact on the field of robotics and edge AI. By bridging the gap between algorithmic complexity and hardware practicality, Xu’s innovations promise to advance real-world deployment of AMRs in logistics, surveillance, and autonomous navigation. His research continues to push the boundaries of low-power, high-performance computing for intelligent systems, making him a rising figure in the intersection of robotics and VLSI design.
Research Focus
Key Achievements
Top Papers
- 1