Papers
1
Total Citations
32
H-Index
1
About
Gao Xu is a researcher in computer vision and autonomous systems, with a primary focus on semantic segmentation for robot navigation. His most influential work, "THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images" (2023), has garnered 32 citations and introduces a novel architecture that efficiently fuses RGB and depth data for real-time road-scene understanding. This contribution addresses critical challenges in autonomous driving by enabling robust segmentation under varying lighting and weather conditions. Xu’s research bridges the gap between deep learning and practical robotics, emphasizing lightweight, cascaded network designs that balance accuracy and computational efficiency. His work is particularly notable for its asymptotic learning approach, which progressively refines segmentation outputs through two-layer hop connections. With a growing citation impact, Gao Xu is establishing himself as an emerging voice in embodied AI and scene parsing, where his methods are directly applicable to driver-assistance systems and mobile robot perception.
Research Focus
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Top Papers
- 1