Accurate registration of 2-D imagery with point clouds is a key technology for image-Light Detection and Ranging (LiDAR) point cloud fusion, camera to laser scanner calibration, and camera localization. Despite continuous improvements, automatic registration of 2-D and 3-D data without using additional textured information still faces great challenges. In this article, we propose a new 2-D–3-D registration method to estimate 2-D–3-D line feature correspondences and the camera pose in untextured point clouds of structured environments. Specifically, we first use geometric constraints between vanishing points and 3-D parallel lines to compute all feasible camera rotations. Then, we utilize a hypothesis testing strategy to estimate the 2-D–3-D line correspondences and the translation vector. By checking the consistency with computed correspondences, the best rotation matrix can be found. Finally, the camera pose is further refined using nonlinear optimization with all the 2-D–3-D line correspondences. The experimental results demonstrate the effectiveness of the proposed method on the synthetic and real data set (outdoors and indoors) with repeated structures and rapid depth changes.

中文翻译：

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结构化环境中基于线的 2D-3D 配准和相机定位

将二维图像与点云准确配准是图像光检测和测距 (LiDAR) 点云融合、相机到激光扫描仪校准以及相机定位的关键技术。尽管不断改进，但在不使用额外纹理信息的情况下自动配准 2-D 和 3-D 数据仍然面临巨大挑战。在本文中，我们提出了一种新的 2-D-3-D 配准方法来估计 2-D-3-D 线特征对应关系和结构化环境的无纹理点云中的相机姿态。具体来说，我们首先使用消失点和 3-D 平行线之间的几何约束来计算所有可行的相机旋转。然后，我们利用假设检验策略来估计 2-D-3-D 线对应和平移向量。通过检查与计算的对应关系的一致性，可以找到最佳旋转矩阵。最后，使用具有所有 2-D-3-D 线对应关系的非线性优化进一步细化相机姿态。实验结果证明了所提出的方法在具有重复结构和快速深度变化的合成和真实数据集（室外和室内）上的有效性。