6D Object Pose Estimation using Keypoints and Part Affinity Fields
This work addresses object pose estimation for autonomous service robots, presenting an incremental improvement by adapting human pose estimation techniques to this domain.
The paper tackles 6D object pose estimation from RGB images for autonomous robots by proposing a two-step pipeline using keypoints and Part Affinity Fields (PAFs) based on OpenPose, achieving accuracy on par with recent methods on the YCB-Video dataset.
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.