6D Pose Estimation Based on 3D Edge Binocular Reprojection Optimization for Robotic Assembly

Accurate 6D pose estimation of object is important for robot assembly. This letter presents a novel method for achieving high precision 6D pose estimation by exploiting the reprojection of 3D edges onto binocular RGB image pairs. Our proposed method encompasses three phases: detection, pose initialization, and pose refinement. In the detection phase, an existing detector is employed to identify the objects within the image pairs. Subsequently, the object image patch of interest is extracted and fed into an encoder-decoder network that leverages edge maps and RGB images for the purpose of initial pose estimation. To refine the initial pose and achieve precise 6D pose estimation, we introduce a novel binocular edge-map-based nonlinear optimization technique. Our primary contributions entail an improved initial pose estimation network and a novel pose optimization technique. The improved network is dedicated to enhancing the accuracy of initial pose estimation, while the optimization technique focuses on refining the precision of the estimations. Experimental results demonstrate the effectiveness of our method, yielding an average translation precision of 0.48 mm and rotation precision of 0.45 degrees. Consequently, our proposed method can be seamlessly integrated into robotic manipulation platforms to successfully execute diverse assembly tasks.