Expeditious Object Pose Estimation for Autonomous Robotic Grasping

Abstract

The ability of a robot to sense and “perceive" its surroundings to interact and influence various objects of interest by grasping them, using vision-based sensors is the main principle behind vision based Autonomous Robotic Grasping. To realise this task of autonomous object grasping, one of the critical sub-tasks is the 6D Pose Estimation of a known object of interest from sensory data in a given environment. The sensory data can include RGB images and data from depth sensors, but determining the object’s pose using only a single RGB image is cost-effective and highly desirable in many applications. In this work, we develop a series of convolutional neural network-based pose estimation models without post-refinement stages, designed to achieve high accuracy on relevant metrics for efficiently estimating the 6D pose of an object, using only a single RGB image. The designed models are incorporated into an end-to-end pose estimation pipeline based on Unity and ROS Noetic, where a UR3 Robotic Arm is deployed in a simulated pick-and-place task. The pose estimation performance of the different models is compared and analysed in both same-environment and cross-environment cases utilising synthetic RGB data collected from cluttered and simple simulation scenes constructed in Unity Environment. In addition, the developed models achieved high Average Distance (ADD) metric scores greater than 93% for most of the real-life objects tested in the LINEMOD dataset and can be integrated seamlessly with any robotic arm for estimating 6D pose from only RGB data, making our method effective, efficient and generic.