A deep learning-enabled visual-inertial fusion method for human pose estimation in occluded human-robot collaborative assembly scenarios

Abstract

In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.