Peng-Xia Cao

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About

Peng-Xia Cao is a researcher in robotics and computer vision, with a primary focus on stereo vision systems for autonomous object positioning. Her most cited work, "Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching" (2020), has garnered 24 citations, establishing a foundation for efficient depth perception in robotic applications. Cao's major contribution lies in developing a coarse-fine stereo matching algorithm that balances computational speed and accuracy, enabling robots to precisely locate objects in three-dimensional space using binocular cameras. This method addresses critical challenges in real-time robotic manipulation, such as reducing mismatches in textureless or repetitive environments. Her work has practical implications for industrial automation, service robotics, and autonomous navigation, where reliable depth estimation is essential. By optimizing the trade-off between processing efficiency and positional precision, Cao's research advances the integration of vision-guided systems into dynamic, unstructured settings. Her contributions continue to influence subsequent studies in robotic perception, particularly in improving the robustness of stereo matching under varied lighting and occlusion conditions.