From what I gather, they are capturing the velodyne data and turning it into a thinned point cloud of the areas of the USA in which they drive. Within this point cloud they can then do effectively a least-squares fit of the real-time view into the historical view and get another data source for localisation. Of course, there's GPS/IMU fusion too, but this sort of SLAM approach is fairly robust under GPS loss so combining them all in a filter is pretty standard practice. The scale of Google's data dwarfs most research projects, however.