Luoying Hao

University of Chinese Academy of Sciences, Chinese Academy of Sciences

Papers

Total Citations

H-Index

About

Luoying Hao is a robotics and computer vision researcher whose work focuses on enabling robust visual-inertial Simultaneous Localization and Mapping (SLAM) systems, particularly under challenging environmental conditions. Hao’s primary contributions lie in developing algorithms that allow autonomous systems—such as robots and wearable devices—to maintain accurate spatial awareness when traditional methods fail. Their most cited work, “LMVI-SLAM: Robust Low-Light Monocular Visual-Inertial Simultaneous Localization and Mapping” (2019, 19 citations), directly addresses a critical weakness in existing SLAM systems: performance degradation in low-light environments. By integrating visual and inertial sensor data more effectively, Hao’s approach significantly reduces drift and failure rates, making it highly relevant for real-world applications like nighttime navigation or indoor robotics. Additionally, Hao proposed a “Lifted Semi-Direct Monocular Visual Odometry” method that enhances computational efficiency for embedded platforms, balancing accuracy with the limited processing power of mobile systems. This work demonstrates a clear focus on practical, deployable solutions for autonomous navigation. Hao’s research is foundational for students and engineers working on robust perception systems, offering proven techniques to overcome sensor limitations in real-world, low-visibility scenarios.