Papers
3
Total Citations
540
H-Index
3
About
Shaozu Cao is a robotics researcher specializing in state estimation, sensor fusion, and autonomous navigation. His work centers on developing robust frameworks that enable robots to perceive and understand their environments with high precision, even in challenging real-world conditions. Cao's most influential contributions include a pair of optimization-based frameworks for multi-sensor pose estimation, published in 2019. The first addresses local odometry estimation by elegantly unifying diverse sensor modalities — including cameras, IMUs, and LiDAR — into a single, flexible optimization pipeline, earning 296 citations. Its companion framework extends this approach to global pose estimation, tackling the critical challenge of achieving both locally accurate and globally drift-free state estimation, accumulating over 205 citations. Together, these works represent a significant step toward generalizable, platform-agnostic autonomy. The continued academic engagement with his 2025 journal extension of the global pose estimation framework demonstrates the lasting relevance of his foundational ideas. With a cumulative citation count exceeding 500 across his key works, Cao has established himself as a meaningful contributor to the autonomous systems community, offering practical, scalable solutions to one of robotics' most fundamental problems.
Research Focus
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Top Papers
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