Erliang Yao

Xi'an High Tech University, Université de Technologie de Compiègne

Papers

Total Citations

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About

Erliang Yao’s research career bridges two distinct eras of robotics and computer vision, from foundational sensor design to modern robust localization. His early work on perception sensors for mobile robots, published in 1997, laid critical groundwork for how autonomous systems interpret their environment, demonstrating an enduring commitment to practical, real-world navigation challenges. Two decades later, Yao made a significant leap forward with his 2018 paper on robust RGB-D visual odometry based on edges and points, which has since garnered 22 citations. This contribution is particularly notable for its elegant fusion of edge and point features, enabling more reliable camera tracking in low-texture or poorly lit environments—a persistent problem in visual SLAM. By addressing the fragility of purely point-based methods, Yao’s approach has influenced subsequent research in indoor and warehouse robotics. Though his publication footprint is selective, each work reflects a deep understanding of sensor limitations and algorithmic resilience. For students and researchers exploring visual odometry, Yao’s trajectory offers a valuable lesson: impactful research often comes from solving specific, stubborn problems with clarity and precision, rather than chasing broad trends.