Yajun Ha

Papers

Total Citations

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About

Yajun Ha is a leading researcher in robotics perception, 3D mapping, and hardware-efficient computer vision. His work centers on developing intelligent systems that can robustly understand and navigate complex environments, with a particular focus on the intersection of algorithmic innovation and practical hardware implementation. Ha’s most-cited paper, “Hierarchical topometric representation of 3D robotic maps” (2021, 23 citations), introduces a novel framework that bridges metric and topological mapping, enabling robots to efficiently represent and reason about large-scale spaces—a foundational contribution to autonomous navigation. He also advanced stereo matching through “CLIF: Cross-Layer Information Fusion for Stereo Matching and its Hardware Implementation” (2021, 4 citations), which demonstrates how fusing multi-level features can improve depth estimation accuracy while maintaining low computational complexity, a critical requirement for real-time systems in robotics and autonomous driving. By addressing the performance gap in stereo algorithms through cross-layer awareness, Ha’s work directly impacts the deployment of intelligent systems in resource-constrained settings. His research continues to shape how robots perceive and interact with the world, making him a key figure in the evolution of efficient, scalable robotic intelligence.