  Estimating the state of a deformable object is crucial for robotic
manipulation, yet accurate tracking is challenging when the object is
partially-occluded. To address this problem, we propose an occlusion-robust
RGBD sequence tracking framework based on Coherent Point Drift (CPD). To
mitigate the effects of occlusion, our method 1) Uses a combination of locally
linear embedding and constrained optimization to regularize the output of CPD,
thus enforcing topological consistency when occlusions create disconnected
pieces of the object; 2) Reasons about the free-space visible by an RGBD sensor
to better estimate the prior on point location and to detect tracking failures
during occlusion; and 3) Uses shape descriptors to find the most relevant
previous state of the object to use for tracking after a severe occlusion. Our
method does not rely on physics simulation or a physical model of the object,
which can be difficult to obtain in unstructured environments. Despite having
no physical model, our experiments demonstrate that our method achieves
improved accuracy in the presence of occlusion as compared to a physics-based
CPD method while maintaining adequate run-time.
