Xiaoyi Yan

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About

Xiaoyi Yan is a researcher specializing in indoor navigation and multi-sensor fusion positioning, with a particular focus on enhancing the accuracy and reliability of mobile robot localization. Their major contributions center on developing integrated navigation systems that combine light detection and ranging (LiDAR) with inertial measurement units (IMUs) to overcome the persistent challenges of low precision and accumulated errors in indoor environments. Yan’s 2020 paper on “Indoor multi-sensor fusion positioning based on federated filtering” has garnered 24 citations, demonstrating its influence in the field. Their subsequent work on LiDAR/IMU-integrated navigation for indoor mobile robots, which introduced a voxel-scale-invariant feature transform (SIFT) method for feature extraction, has received 11 citations and addresses critical limitations in real-time positioning. By advancing federated filtering techniques and sensor fusion architectures, Yan has contributed practical solutions for autonomous robotics and indoor positioning systems. Their research is particularly valuable for applications in warehouse automation, service robotics, and smart environments where GPS-denied operation is essential. Yan’s work continues to shape the development of robust, low-latency navigation systems for mobile platforms.