Jing Luo

Papers

Total Citations

H-Index

About

Jing Luo is a researcher specializing in mobile robotics, indoor localization, and sensor fusion technologies. Their work centers on advancing the navigation capabilities of autonomous mobile robots, with a particular focus on developing sophisticated algorithms that integrate multiple data sources to achieve robust and accurate positioning in complex indoor environments. Luo's most notable contribution is a proposed Multiple Data Fusion (MDF) method that leverages an Extended Kalman Filter (EKF) framework to synthesize data from multiple sensors simultaneously, significantly outperforming traditional single-sensor navigation approaches. This research addresses a critical challenge in robotics: the inherent limitations and uncertainties that arise when relying on any single sensing modality for indoor localization. Published in 2021, this work has already garnered 22 citations, reflecting its meaningful impact within the robotics and autonomous systems community. By enhancing localization reliability and navigation performance, Luo's research contributes foundational knowledge applicable to service robots, autonomous vehicles, and intelligent warehouse systems. Their expertise in probabilistic estimation and multi-modal sensor integration positions them as a valuable contributor to the evolving field of intelligent autonomous navigation.