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Simple and efficient registration of 3D point cloud and image data for an indoor mobile mapping system
Journal of the Optical Society of America A ( IF 1.9 ) Pub Date : 2021-03-31 , DOI: 10.1364/josaa.414042
Hao Ma , Keke Liu , Jingbin Liu , Hongyu Qiu , Dong Xu , Zemin Wang , Xiaodong Gong , Sheng Yang

Registration of 3D lidar point clouds with optical images is critical in the combination of multisource data. Geometric misalignment originally exists in the pose data between lidar point clouds and optical images. To improve the accuracy of the initial pose and the applicability of the integration of 3D points and image data, we develop a simple but efficient registration method. We first extract point features from lidar point clouds and images: point features are extracted from single-frame lidar and point features are extracted from images using a classical Canny operator. The cost map is subsequently built based on Canny image edge detection. The optimization direction is guided by the cost map, where low cost represents the desired direction, and loss function is also considered to improve the robustness of the proposed method. Experiments show positive results.

中文翻译:

室内移动制图系统的3D点云和图像数据的简单高效注册

3D激光雷达点云与光学图像的配准对于多源数据的组合至关重要。激光雷达点云和光学图像之间的姿态数据中最初存在几何失准。为了提高初始姿势的准确性以及3D点和图像数据集成的适用性,我们开发了一种简单而有效的套准方法。我们首先从激光雷达点云和图像中提取点特征:使用经典的Canny算子从单帧激光雷达中提取点特征,并从图像中提取点特征。随后基于Canny图像边缘检测构建成本图。优化方向由成本图指导,其中低成本表示所需方向,损失函数也被认为可以提高所提出方法的鲁棒性。
更新日期:2021-04-01
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