Yonglong Huang
Papers
2
Total Citations
7
H-Index
1
About
Yonglong Huang is a robotics researcher whose work focuses on autonomous navigation, sensor fusion, and human-robot interaction. His key contributions lie in developing robust localization and obstacle avoidance systems for mobile robots operating in dynamic, real-world environments. In his highly cited 2019 paper, "Real-time Obstacle Avoidance and Person Following Based on Adaptive Window Approach," Huang introduced a laser-based people tracking and following method that enables robots to safely navigate around obstacles while maintaining a human following behavior—a foundational capability for collaborative and service robotics. More recently, his 2025 work, "IMU/Camera/GNSS/UWB Integrated Localization Method Based on Factor Graph Optimization," addresses critical challenges in multi-sensor fusion, including satellite navigation failures, inertial drift, visual degradation, and UWB non-line-of-sight errors. By leveraging factor graph optimization, Huang’s approach significantly enhances positioning accuracy and robustness in complex environments. With over 7 combined citations, his research is gaining traction among scholars working on autonomous navigation and sensor integration. Huang’s work is particularly notable for its practical focus on overcoming real-world limitations, making his methods directly applicable to the next generation of autonomous mobile robots.
Research Focus
Key Achievements
Top Papers
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- 2