Xiangyang Gong
Papers
2
Total Citations
10
H-Index
2
About
Xiangyang Gong is a researcher whose work bridges the critical domains of sensor fusion and 3D perception for robotics and autonomous systems. His key contributions lie in developing efficient, real-time algorithms for attitude determination and advancing transformer-based architectures for point cloud segmentation. Gong’s 2018 paper on a simplified attitude determination algorithm using accelerometer and magnetometer data introduced a novel analytic solution to Wahba’s problem, achieving extremely low execution time—a vital advancement for industrial robotics and consumer electronics. This work has garnered 7 citations, reflecting its practical impact on embedded systems. More recently, in 2024, Gong proposed the Field-Aware Transformer (FAT) for point cloud segmentation, which innovatively adapts attention fields to different regions of a point cloud, addressing a key limitation of uniform attention in existing transformer models. This work, with 3 citations, demonstrates his continued drive to enhance perception for autonomous driving and robotics. Gong’s research is characterized by a focus on computational efficiency and adaptive feature learning, making his contributions directly applicable to real-world industrial challenges.
Research Focus
Key Achievements
Top Papers
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- 2