Scholarlink

<p>In the development of an autonomous driving system, it is essential to find accurate transformation between an embedded GNSS/INS (EGI), which measures global navigation information, and cameras that visually capture the surrounding environment. Since EGI cannot directly perceive the surrounding environment but only measures the motion of the ego vehicle, extrinsic calibration between the two sensors is typically accomplished by comparing the motion of each sensor. However, the conventional motion-based methods that rely solely on comparing camera motion estimated from just two views, without considering constraints observed in multiple views, may result in unsatisfactory performance. In this study, we propose a refined EGI-camera calibration pipeline that comprises a coarse calibration step using an improved motion-based method and a fine calibration step that determines extrinsic parameters to minimize reprojection error directly without calculating camera motion. Through Monte Carlo simulations and real vehicle tests, we demonstrate that the proposed pipeline yields more accurate results than the conventional motion-based method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Two-Stage Scheme for Motion-based Extrinsic Calibration Between Monocular Cameras and GNSS/INS

  • 期刊论文

  • 发表日期：

    2025年12月5日

查看全文
  • Yeongkwon Choe,
  • Kyoung Won Min

摘要

In the development of an autonomous driving system, it is essential to find accurate transformation between an embedded GNSS/INS (EGI), which measures global navigation information, and cameras that visually capture the surrounding environment. Since EGI cannot directly perceive the surrounding environment but only measures the motion of the ego vehicle, extrinsic calibration between the two sensors is typically accomplished by comparing the motion of each sensor. However, the conventional motion-based methods that rely solely on comparing camera motion estimated from just two views, without considering constraints observed in multiple views, may result in unsatisfactory performance. In this study, we propose a refined EGI-camera calibration pipeline that comprises a coarse calibration step using an improved motion-based method and a fine calibration step that determines extrinsic parameters to minimize reprojection error directly without calculating camera motion. Through Monte Carlo simulations and real vehicle tests, we demonstrate that the proposed pipeline yields more accurate results than the conventional motion-based method.