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.
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