  Robots working in human environments often encounter a wide range of
articulated objects, such as tools, cabinets, and other jointed objects. Such
articulated objects can take an infinite number of possible poses, as a point
in a potentially high-dimensional continuous space. A robot must perceive this
continuous pose to manipulate the object to a desired pose. This problem of
perception and manipulation of articulated objects remains a challenge due to
its high dimensionality and multi-modal uncertainty. In this paper, we propose
a factored approach to estimate the poses of articulated objects using an
efficient nonparametric belief propagation algorithm. We consider inputs as
geometrical models with articulation constraints, and observed RGBD sensor
data. The proposed framework produces object-part pose beliefs iteratively. The
problem is formulated as a pairwise Markov Random Field (MRF) where each hidden
node (continuous pose variable) is an observed object-part's pose and the edges
denote the articulation constraints between the parts. We propose articulated
pose estimation by Pull Message Passing algorithm for Nonparametric Belief
Propagation (PMPNBP) and evaluate its convergence properties over scenes with
articulated objects.
