  Quantification of uncertainty in point cloud matching is critical in many
tasks such as pose estimation, sensor fusion, and grasping. Iterative closest
point (ICP) is a commonly used pose estimation algorithm which provides a point
estimate of the transformation between two point clouds. There are many sources
of uncertainty in this process that may arise due to sensor noise, ambiguous
environment, and occlusion. However, for safety critical problems such as
autonomous driving, a point estimate of the pose transformation is not
sufficient as it does not provide information about the multiple solutions.
Current probabilistic ICP methods usually do not capture all sources of
uncertainty and may provide unreliable transformation estimates which can have
a detrimental effect in state estimation or decision making tasks that use this
information. In this work we propose a new algorithm to align two point clouds
that can precisely estimate the uncertainty of ICP's transformation parameters.
We develop a Stein variational inference framework with gradient based
optimization of ICP's cost function. The method provides a non-parametric
estimate of the transformation, can model complex multi-modal distributions,
and can be effectively parallelized on a GPU. Experiments using 3D kinect data
as well as sparse indoor/outdoor LiDAR data show that our method is capable of
efficiently producing accurate pose uncertainty estimates.
