  Centimeter level globally accurate and consistent maps for autonomous
vehicles navigation has long been achieved by on board real-time
kinematic(RTK)-GPS in open areas. However when dealing with urban environments,
GPS will experience multipath and blockage in urban canyon, under bridges,
inside tunnels and in underground environments. In this paper we present
strategies to efficiently register local maps in geographical coordinate
systems through the tactical integration of GPS and information extracted from
precisely geo-referenced high resolution aerial orthogonal imagery. Dense lidar
point clouds obtained from moving vehicle are projected down to horizontal
plane, accurately registered and overlaid on aerial orthoimagery. Sparse,
robust and long-term pole-like landmarks are used as anchor points to link
lidar and aerial image sensing, and constrain the spatial uncertainties of
remaining lidar points that cannot be directly measured and identified. We
achieved 15-75cm absolute average global accuracy using precisely
geo-referenced aerial imagery as ground truth. This is valuable in enabling the
fusion of ground vehicle on-board sensor features with features extracted from
aerial images such as traffic and lane markings. It is also useful for
cooperative sensing to have an unbiased and accurate global reference.
Experimental results are presented demonstrating the accuracy and consistency
of the maps when operating in large areas.
