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About

Jiaxiang Hu is a robotics researcher whose work focuses on advancing autonomous navigation and perception systems. His primary research areas include multi-robot coordination, LiDAR-based scene understanding, and multi-sensor fusion for state estimation. Hu’s most notable contribution is the development of a multi-robot navigation framework that integrates graph attention neural networks with hierarchical motion planning, enabling more efficient and intelligent coordination among robotic teams. This work has garnered 4 citations and represents a significant step toward scalable multi-agent systems. In the domain of perception, Hu introduced MosViT, a vision transformer architecture designed for moving object segmentation from LiDAR point clouds, addressing the critical challenge of extracting spatial-temporal information from sequential data while mitigating dataset scarcity. This paper has received 2 citations. Additionally, Hu developed M³LVI, a tightly coupled LiDAR-visual-inertial odometry system that leverages multiple features, metrics, and loop closures for high-accuracy state estimation and mapping. Built on a factor graph framework, this work demonstrates Hu’s expertise in robust sensor fusion for autonomous systems. Through these contributions, Hu is establishing himself as an emerging researcher in robotics, with a focus on creating more perceptive and coordinated autonomous systems.