  The task of 6D object pose estimation from RGB images is an important
requirement for autonomous service robots to be able to interact with the real
world. In this work, we present a two-step pipeline for estimating the 6 DoF
translation and orientation of known objects. Keypoints and Part Affinity
Fields (PAFs) are predicted from the input image adopting the OpenPose CNN
architecture from human pose estimation. Object poses are then calculated from
2D-3D correspondences between detected and model keypoints via the PnP-RANSAC
algorithm. The proposed approach is evaluated on the YCB-Video dataset and
achieves accuracy on par with recent methods from the literature. Using PAFs to
assemble detected keypoints into object instances proves advantageous over only
using heatmaps. Models trained to predict keypoints of a single object class
perform significantly better than models trained for several classes.
