3D Whole-body Pose Estimation Using Graph High-Resolution Network for Humanoid Robot Teleoperation

In the realm of robotics, teleoperation plays a pivotal role in performing high-risk or intricate tasks, and obtaining precise 3D whole-body pose is crucial for this purpose. Traditional two-stage methods have limitations in estimating different body parts, leading to complex systems and higher estimation errors. In order to address these issues,the paper introduces a novel framework called Graph High-Resolution Network(GraphHRNet) for accurate 3D whole-body pose estimation, which is essential for the teleoperation of humanoid robots. GraphHRNet effectively captures global structural information and local details by integrating a High-Resolution Module and a Multi-branch Regression Module. The High-Resolution Module utilizes an enhanced graph convolution kernel to fuse multi-scale features, capturing global information, while the Multi-branch Regression Module focuses on refining and predicting accurate 3D coordinates for intricate body parts such as hands and face. Experimental results on the H3WB dataset demonstrate that GraphHRNet surpasses state-of-the-art(SOTA) methods in 3D whole-body pose estimation , significantly improving performance. Furthermore, the paper explores the potential application of this approach in a teleoperation system for humanoid robots, providing an intuitive and high-fidelity solution for remotely executing complex tasks.