  Augmented reality (AR) has gained increasingly attention from both research
and industry communities. By overlaying digital information and content onto
the physical world, AR enables users to experience the world in a more
informative and efficient manner. As a major building block for AR systems,
localization aims at determining the device's pose from a pre-built "map"
consisting of visual and depth information in a known environment. While the
localization problem has been widely studied in the literature, the "map" for
AR systems is rarely discussed. In this paper, we introduce the AR Map for a
specific scene to be composed of 1) color images with 6-DOF poses; 2) dense
depth maps for each image and 3) a complete point cloud map. We then propose an
efficient end-to-end solution to generating and evaluating AR Maps. Firstly,
for efficient data capture, a backpack scanning device is presented with a
unified calibration pipeline. Secondly, we propose an AR mapping pipeline which
takes the input from the scanning device and produces accurate AR Maps.
Finally, we present an approach to evaluating the accuracy of AR Maps with the
help of the highly accurate reconstruction result from a high-end laser
scanner. To the best of our knowledge, it is the first time to present an
end-to-end solution to efficient and accurate mapping for AR applications.
