  We present an iterative overlap estimation technique to augment existing
point cloud registration algorithms that can achieve high performance in
difficult real-world situations where large pose displacement and
non-overlapping geometry would otherwise cause traditional methods to fail. Our
approach estimates overlapping regions through an iterative Expectation
Maximization procedure that encodes the sensor field-of-view into the
registration process. The proposed technique, Expected Overlap Estimation
(EOE), is derived from the observation that differences in field-of-view
violate the iid assumption implicitly held by all maximum likelihood based
registration techniques. We demonstrate how our approach can augment many
popular registration methods with minimal computational overhead. Through
experimentation on both synthetic and real-world datasets, we find that adding
an explicit overlap estimation step can aid robust outlier handling and
increase the accuracy of both ICP-based and GMM-based registration methods,
especially in large unstructured domains and where the amount of overlap
between point clouds is very small.
