  Distribution-to-distribution (D2D) point cloud registration techniques such
as the Normal Distributions Transform (NDT) can align point clouds sampled from
unstructured scenes and provide accurate bounds of their own solution error
covariance -- an important feature for safety-of-life navigation tasks. D2D
methods rely on the assumption of a static scene and are therefore susceptible
to bias from range-shadowing, self-occlusion, moving objects, and distortion
artifacts as the recording device moves between frames. Deep Learning-based
approaches can achieve higher accuracy in dynamic scenes by relaxing these
constraints, however, DNNs produce uninterpretable solutions which can be
problematic from a safety perspective. In this paper, we propose a method of
down-sampling LIDAR point clouds to exclude voxels that violate the assumption
of a static scene and introduce error to the D2D scan matching process. Our
approach uses a solution consistency filter -- identifying and suppressing
voxels where D2D contributions disagree with local estimates from a
PointNet-based registration network. Our results show that this technique
provides significant benefits in registration accuracy, and is particularly
useful in scenes containing dense foliage.
