[Paper] Automatic Calibration of an in-Vehicle Camera based on Structure from Motion

DOI Web Site 参考文献28件
  • Hayakawa Kazutaka
    Graduate School of Data Science, Shiga University AISIN CORPORATION
  • Nishio Haruki
    Graduate School of Data Science, Shiga University Center for Ecological Research, Kyoto University
  • Nakagawa Yoshiaki
    Graduate School of Data Science, Shiga University Waseda University
  • Sato Tomokazu
    Graduate School of Data Science, Shiga University

説明

<p>In this article, we propose a novel method to automatically estimates camera posture parameters of an in-vehicle camera unit. Several methods have already been proposed which estimate those parameters mainly relying on known texture patterns on the ground (e.g. like road signs, lane markers). Unlike conventional methods, our method achieves camera calibration without given texture patterns, by using the camera trajectory estimated by Structure from Motion (SfM) as a clue. As another contribution, we have evaluated the effectiveness of multiple techniques that are empirically known to improve the robustness and accuracy of SfM but have not been well discussed in the literature. In an experiment, we show that the pose parameters can automatically be estimated in the real driving environments, and the results from the proposed and compared methods are quantitatively evaluated.</p>

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