Yingping Huang
Papers
2
Total Citations
34
H-Index
2
About
Yingping Huang is a researcher at the forefront of autonomous vehicle perception and robotic navigation, with a primary focus on visual odometry (VO) and ego-motion estimation. Their work addresses a critical challenge in modern robotics: enabling agents—from self-driving cars to mobile robots—to accurately track their own movement using only camera input. In their highly cited 2021 paper (19 citations), Huang pioneered a novel end-to-end network that combines recurrent convolutional neural networks with optical flow learning, advancing monocular VO by eliminating the need for traditional geometric pipelines. Earlier foundational work in 2016 (15 citations) established a robust stereo-based method for 6-Degrees-of-Freedom ego-motion estimation in complex urban environments, integrating optical flow analysis with outlier rejection to achieve high precision. These contributions have been instrumental in making visual odometry more reliable for real-world autonomous systems. With a combined citation impact exceeding 34 citations on these key works, Huang’s research continues to shape how machines perceive and navigate their surroundings, bridging deep learning and classical computer vision for safer, more capable autonomous agents.
Research Focus
Key Achievements
Top Papers
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