6DoF Graspability Evaluation on a Single Depth Map

Abstract

Robot-based picking of diverse objects remains one of the critical challenges in the field of robotic automation research. In the automation of vision-based picking, methods based on large-scale pre-training or object shape models have been proposed. However, in many real industrial settings, it is often difficult to prepare the necessary pre-trained models or object shape models. One approach that does not require pre-training or object shape models is fast graspability evaluation (FGE). FGE efficiently detects grasp positions by convolving depth images with a cross-sectional model of the robot hand. However, a limitation of FGE is that it requires the grasp direction to align with the sensor’s line of sight, and it can only detect grasp positions with up to 4 degrees of freedom (DoF). In this paper, we extend FGE to 6DoF grasp position detection by calculating the 2 additional DoFs of grasp posture based on the normal directions around the object’s grasp region, obtained from the depth image. In picking experiments using three types of actual industrial parts, a comparison between the proposed method and FGE confirmed an average improvement of 10% in grasp success rates.