  Robots working in human environments must be able to perceive and act on
challenging objects with articulations, such as a pile of tools. Articulated
objects increase the dimensionality of the pose estimation problem, and partial
observations under clutter create additional challenges. To address this
problem, we present a generative-discriminative parts-based recognition and
localization method for articulated objects in clutter. We formulate the
problem of articulated object pose estimation as a Markov Random Field (MRF).
Hidden nodes in this MRF express the pose of the object parts, and edges
express the articulation constraints between parts. Localization is performed
within the MRF using an efficient belief propagation method. The method is
informed by both part segmentation heatmaps over the observation, generated by
a neural network, and the articulation constraints between object parts. Our
generative-discriminative approach allows the proposed method to function in
cluttered environments by inferring the pose of occluded parts using hypotheses
from the visible parts. We demonstrate the efficacy of our methods in a
tabletop environment for recognizing and localizing hand tools in uncluttered
and cluttered configurations.
