6D Pose Estimation for Precision Assembly

Abstract

The assembly of 3D products with complex geometry and material, such as a concentrator photovoltaics solar panel unit, is typically conducted manually. This results in low efficiency, precision and throughput. This study is motivated by an actual industrial need and targeted towards automation of the currently manual assembly process. By replacing the manual assembly with robotic assembly systems, the efficiency and throughput could be improved. Prior to assembly, it is essential to estimate the pose of the objects to be assembled with high precision. The choice of the machine vision is important and plays a critical role in the overall accuracy of such a complex task. Therefore, this work focuses on the 6D pose estimation for precision assembly utilizing a 3D vision sensor. The sensor we use is a 3D structured light scanner which can generate high quality point cloud data in addition to 2D images. A 6D pose estimation method is developed for an actual industrial solar-cell object, which is one of the four objects of an assembly unit of concentrator photovoltaics solar panel. The proposed approach is a hybrid approach where a mask R-CNN network is trained on our custom dataset and the trained model is utilized such that the predicted 2D bounding boxes are used for point cloud segmentation. Then, the iterative closest point algorithm is used to estimate the object's pose by matching the CAD model to the segmented object in point cloud.