Boyang Xing

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About

Boyang Xing is a researcher specializing in autonomous navigation, multi-sensor fusion, and robotic perception, with a particular focus on indoor localization systems for micro air vehicles (MAVs) and legged robots. His most influential work, "Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles" (2018, 39 citations), introduces a novel algorithm that fuses ArUco markers with multi-sensor data to enable robust, real-time indoor positioning. A standout contribution is his online ArUco mapping method, which uses Grubbs criterion and K-mean clustering to correct map distortions—a critical advancement for reliable MAV flight in GPS-denied environments. Xing also explores path planning for quadruped robots, integrating SLAM-based globally mapping localization to enhance autonomous walking in complex terrain. His earlier work on monocular camera-based UAV pose estimation (2016, 5 citations) laid groundwork for visual navigation in known environments. Collectively, Xing’s research bridges the gap between aerial and ground robotics, offering scalable solutions for autonomous systems. His work is highly cited in the fields of robotics and sensor fusion, reflecting its practical impact on real-world navigation challenges.