A 6-dimensional pose estimation method combining sparse viewpoint classification initialization and optical flow-guided iterative refinement

Electronic equipment is typically a complex and high-precision electromechanical system, where the routing and bundling of Radio Frequency (RF) cables are crucial to equipment performance. Traditional assembly methods require workers to assemble according to the assembly process card, which can easily lead to incorrect or missing assembly, poor assembly consistency, and low efficiency. Augmented Reality (AR) assembly guidance can effectively improve efficiency and reduce errors. 6-dimensional (6D) pose estimation is a key technology for AR assembly guidance. In the assembly process of complex electronic products, existing deep learning methods suffer from poor tracking and localization robustness and real-time performance due to factors such as arm occlusion, resulting in slow tracking recovery. This article proposes a two-stage real-time 6D pose estimation method from coarse to fine, which can estimate the pose of target objects in complex backgrounds at a speed of about 20 frames per second and quickly recover after tracking target loss. The real-time and effectiveness were verified through experiments on the red squirrel and electronic chassis.

Authors

• Huan Yang
• Yue Wang University of Central Florida
• Xinghang Yin
• Yongxu Liu Xidian University
• Peng Wang

Keywords

• Pose estimation
• Augmented reality
• Neural network
• Pose refinement
• Structural reparameterization