Papers
4
Total Citations
10
H-Index
2
About
Minjie Bao is an emerging robotics and embedded systems researcher whose work sits at the intersection of autonomous mobile robotics, simultaneous localization and mapping (SLAM), and hardware-accelerated computing. His research addresses some of the most pressing practical challenges in deploying robots in real-world environments, particularly focusing on impact-aware localization and energy-efficient processing architectures. Bao is perhaps best known for his RIA-CSM series of algorithms, which tackle a critical vulnerability in conventional correlative scan matching: the degraded performance that occurs when robots experience physical impacts in dynamic, people-filled environments. By leveraging heterogeneous multi-core System-on-Chip (SoC) designs, his work achieves real-time robustness while managing computational complexity — a persistent bottleneck in the field. His most-cited paper on RIA-CSM (2022) has garnered 5 citations, with follow-up work extending these contributions to low-cost wheeled robots like cleaning robots. Beyond localization, Bao has made notable strides in FPGA-based hardware acceleration for EKF-SLAM, developing reconfigurable, energy-efficient processors that bring high-frame-rate mapping capabilities to resource-constrained edge devices. His body of work represents a technically rigorous and practically motivated research agenda that is increasingly relevant as autonomous robots enter everyday human spaces.
Research Focus
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Top Papers
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