Shige Meng

South China Robotics Innovative Research Institute

Papers

Total Citations

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About

Shige Meng is a leading researcher in mobile robotics, with a primary focus on sensor fusion and autonomous navigation. Their most cited work, "An EKF-based multiple data fusion for mobile robot indoor localization" (2021, 22 citations), addresses a critical challenge in robotics: achieving reliable indoor localization. Rather than relying on a single sensor, Meng pioneered a multiple data fusion (MDF) method that integrates data from various sensors using an Extended Kalman Filter (EKF). This approach significantly enhances the accuracy and robustness of robot navigation in complex indoor environments. By solving the problem of sensor dependency, Meng’s contributions have provided a practical framework for autonomous systems to operate more reliably. Their work is particularly influential for researchers and engineers developing service robots, autonomous vehicles, and smart logistics systems. With a clear focus on bridging the gap between theoretical sensor fusion and real-world deployment, Shige Meng continues to advance the state of the art in mobile robot localization and navigation.