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Robots working in human environments often encounter a wide range ofarticulated objects, such as tools, cabinets, and other jointed objects. Sucharticulated objects can take an infinite number of possible poses, as a pointin a potentially high-dimensional continuous space. A robot must perceive thiscontinuous pose to manipulate the object to a desired pose. This problem ofperception and manipulation of articulated objects remains a challenge due toits high dimensionality and multi-modal uncertainty. In this paper, we proposea factored approach to estimate the poses of articulated objects using anefficient nonparametric belief propagation algorithm. We consider inputs asgeometrical models with articulation constraints, and observed RGBD sensordata. The proposed framework produces object-part pose beliefs iteratively. Theproblem is formulated as a pairwise Markov Random Field (MRF) where each hiddennode (continuous pose variable) is an observed object-part's pose and the edgesdenote the articulation constraints between the parts. We propose articulatedpose estimation by Pull Message Passing algorithm for Nonparametric BeliefPropagation (PMPNBP) and evaluate its convergence properties over scenes witharticulated objects.

Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagation