  Estimating position and orientation change of a mobile platform from two
consecutive point clouds provided by a high-resolution sensor is a key problem
in autonomous navigation. In particular, scan matching algorithms aim to find
the translation and rotation of the platform such that the two point clouds
coincide. The association of measurements in point cloud one with measurements
in point cloud two is a problem inherent to scan matching. Existing methods
perform non-probabilistic data association, i.e., they assume a single
association hypothesis. This leads to overconfident pose estimates and reduced
estimation accuracy in ambiguous environments. Our probabilistic scan matching
approach addresses this issue by considering all association hypotheses with
their respective likelihoods. We formulate a holistic Bayesian estimation
problem for both data association and pose estimation and present the
corresponding joint factor graph. Near-optimum maximum a posteriori (MAP)
estimates of the sensor pose are computed by performing iterative message
passing on the factor graph. Our numerical study shows performance improvements
compared to non-probabilistic scan matching methods that are based on the
normal distributions transform (NDT) and implicit moving least squares (IMLS).
