Xiaoqiang Teng

Beijing Technology and Business University

Papers

Total Citations

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About

Xiaoqiang Teng is a leading researcher in data-driven inertial navigation, with a focus on advancing mobile computing applications including augmented reality, robotics, and autonomous navigation. His most notable contribution is the development of VANE-IN (Velocity Auto-Encoder for Inertial Navigation), a novel framework that employs a velocity regression network (VRN) to estimate velocities from inertial measurement unit (IMU) data. This work, published in 2024, addresses critical challenges in position determination for mobile systems by improving the accuracy and robustness of inertial navigation in real-world environments. Teng’s research sits at the intersection of machine learning and sensor fusion, where he leverages deep learning to overcome the limitations of traditional inertial navigation systems, such as drift and noise accumulation. His work has garnered attention in the field, with his most-cited paper accumulating 3 citations in a short time, signaling growing impact. Teng’s contributions are particularly valuable for applications requiring precise, continuous positioning without reliance on external signals, making his research foundational for next-generation mobile and robotic systems.