Humanoid Pose Estimation through Synergistic Integration of Computer Vision and Deep Learning Techniques*

This study explores the performance of Convolutional Neural Networks (CNNs) in the context of humanoid robot localization in dynamic environments. Utilizing a front-mounted camera system, initial experiments demonstrate CNNs achieving a 72% accuracy in position and a 92% accuracy rate in orientation with an 8000-image dataset. These results underscore the effectiveness of CNNs in addressing the challenge of precise robot localization. Moreover, the study introduces the YOLO (You Only Look Once) object detection algorithm to further enhance performance. Beyond robotics, this research extends to applications in smartphone navigation, Indoor GPS systems, and drone tracking. The paper provides insights into the methodologies employed and highlights the transformative potential of integrating CNNs into localization tasks.