  In this paper, we derive a probabilistic registration algorithm for object
modeling and tracking. In many robotics applications, such as manipulation
tasks, nonvisual information about the movement of the object is available,
which we will combine with the visual information. Furthermore we do not only
consider observations of the object, but we also take space into account which
has been observed to not be part of the object. Furthermore we are computing a
posterior distribution over the relative alignment and not a point estimate as
typically done in for example Iterative Closest Point (ICP). To our knowledge
no existing algorithm meets these three conditions and we thus derive a novel
registration algorithm in a Bayesian framework. Experimental results suggest
that the proposed methods perform favorably in comparison to PCL
implementations of feature mapping and ICP, especially if nonvisual information
is available.
