A posture detection method for augmented reality–aided assembly based on YOLO-6D

Abstract

The assembly of small batch electrical and mechanical products still relies on manual operation, with the problems of high rechecking time and low assembly efficiency and quality. The augmented reality technology can be used to assist assembly to improve efficiency. In view of the high professionalism, labor, and time cost for the traditional posture detection method, a posture detection method is proposed to match the assembly posture by pre-calibrating the assembly posture template. The YOLO-6D model is brought in to increase the robustness of tracking registration. And the detection from both translation and rotation perspectives is designed to enhance the adaptability to different assembly tasks. For the generation of training datasets, a weighted sampling method based on part features is proposed to optimize the accuracy of position estimation with the limited training samples. Taking the assembly process of a typical electronic product as an example, the developed augmented reality aided assembly system is used to boost the assembly efficiency of the assembly operator effectively as compared to the traditional posture detection method.