Wuju Yang

Inner Mongolia University of Science and Technology

Papers

Total Citations

H-Index

About

Wuju Yang is a researcher at the forefront of intelligent robotics and indoor positioning systems, with a focus on integrating advanced artificial intelligence and sensor fusion to solve real-world navigation challenges. Their major contributions lie in developing high-accuracy, deep learning-driven solutions for autonomous mobile robots. Notably, Yang pioneered the use of GRU neural networks for indoor visible-light 3D positioning, achieving superior precision in complex environments—a work that has garnered 20 citations since 2023. This approach addresses the critical need for reliable robot localization where GPS fails. Earlier, Yang tackled the limitations of traditional SLAM by proposing an improved RBPF-SLAM algorithm for LIDAR-based systems, effectively reducing particle degradation and enhancing mapping consistency, a study cited 9 times. By bridging the gap between deep learning and classical robotics, Yang’s research directly impacts the development of more resilient, self-navigating robots for industrial and service applications. Their work is essential reading for students and engineers seeking to understand how neural architectures can transform indoor positioning accuracy and autonomous navigation.