AR Mapping: Accurate and Efficient Mapping for Augmented Reality

TL;DR

This paper introduces a comprehensive end-to-end method for creating accurate and efficient AR maps, including data capture, mapping, and evaluation, to enhance localization in augmented reality systems.

Contribution

It presents the first complete pipeline for AR mapping that integrates data collection, map generation, and accuracy assessment, improving localization accuracy in AR applications.

Findings

Developed a backpack scanning device with a unified calibration pipeline.

Proposed an end-to-end AR mapping pipeline from data capture to map generation.

Validated AR map accuracy using high-end laser scanner reconstructions.

Abstract

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.