Papers
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3
H-Index
1
About
Yequan Zhao is a rising researcher in the field of autonomous mobile robotics, with a focused expertise in energy-efficient hardware acceleration for real-time localization and mapping. His primary research areas include embedded system-on-chip (SoC) design, FPGA-based accelerators, and the optimization of Extended Kalman Filter Simultaneous Localization and Mapping (EKF-SLAM) algorithms for edge computing applications. Zhao’s major contribution lies in developing a reconfigurable, high-frame-rate, and energy-efficient EKF-SLAM processor that achieves full algorithmic acceleration, enabling autonomous mobile robots to perform precise localization and mapping with minimal power consumption. His work, demonstrated on a ZYNQ-7000 FPGA-based SoC integrating LiDAR and wheel encoder sensors, addresses critical bottlenecks in real-time performance for intelligent edge devices. Although his most-cited papers are recent (2024–2025), they have already garnered attention for their practical impact on autonomous navigation. Zhao’s innovations are particularly notable for balancing computational efficiency with real-time responsiveness, making them highly relevant for next-generation robotics and IoT applications. His research promises to advance the deployment of autonomous systems in resource-constrained environments.
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