6DoF assembly pose estimation dataset for robotic manipulation

Abstract

Robotic assembling is a challenging task that requires cognition and dexterity. In recent years, perception tools have achieved tremendous success in endowing the cognitive capabilities to robots. Although these tools have succeeded in tasks such as detection, scene segmentation, pose estimation and grasp manipulation, the associated datasets and the dataset contents lack crucial information that requires adapting them for assembling pose estimation. Furthermore, existing datasets of object 3D meshes and point clouds are presented in non-canonical view frames and therefore lack information to train perception models that infer on a visual scene. The dataset presents 2 simulated object assembly scenes with RGB-D images, 3D mesh files and ground truth assembly poses as an extension for the State-of-the-Art BOP format. This enables smooth expansion of existing perception models in computer vision as well as development of novel algorithms for estimating assembly pose in robotic assembly manipulation tasks.