Mingyue Zheng

Shandong University of Science and Technology

Papers

Total Citations

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About

Mingyue Zheng is a researcher specializing in robotics and autonomous navigation, with a primary focus on simultaneous localization and mapping (SLAM) and sensor fusion. Her most cited work, "Improved grid mapping technology based on Rao-Blackwellized particle filters and the gradient descent algorithm" (2019), addresses a critical challenge in SLAM: reducing computational complexity while maintaining mapping accuracy. Zheng’s key contribution lies in enhancing the Rao-Blackwellized particle filter (RBPF) framework by integrating a gradient descent algorithm to refine the proposal distribution, thereby minimizing the number of particles required for reliable odometry-based mapping. This innovation significantly lowers computational overhead without sacrificing localization precision, making it highly relevant for resource-constrained robotic platforms. With 5 citations, her work has influenced subsequent studies in efficient SLAM algorithms. Zheng’s research bridges theoretical particle filtering methods with practical robotic applications, offering a scalable solution for real-time environment mapping. Her achievements underscore a commitment to advancing autonomous systems, particularly in optimizing the trade-off between accuracy and computational efficiency—a cornerstone for deploying robots in dynamic, real-world settings.