三维点云中的二维标记检测

特征检测在物体识别、数据配准等应用中具有至关重要的作用。同一场景中不同采集数据的配准和融合,必须已知或者估算不同数据中的共同特征对应点。然而,许多场景缺少有效对应特征点。解决该问题的一种有效的方法是在场景中添加标记以增加特征。文章提出一种在只含有位置信息的三维点云中自动检测二维标记的方法。该方法首先在三维场景添加黑色圆形薄纸片作为二维标记,利用区域增长法将获取的三维场景的点云数据分割成不同类别,然后基于随机抽样一致性算法的扩展方法依次对分割后的点云进行形状拟合,最后通过检测形状检测该二维标记。该方法能够有效地检测出三维场景中的二维标记,并避免了遮挡、形变等问题,为缺少特征的场景提供了简单可行的特征,可广泛应用于数据配准、物体识别、物体追踪、三维重建等领域。

Two Dimensional Marker Detection in Three Dimensional Point Cloud

Feature detection plays an important role in object detection and registration. For data registration, correspondences of different frames in the same scene should be found firstly. However, in many cases, there are not effective correspondences, which leads to incorrect registration. One of the effective solutions is to add marker manually in the scene. A method for detecting 2D marker (a black circle drew on paper for scene, hollow circular for point cloud) automatically in 3D point cloud (3D position information only) was proposed. First of all, add a 2D marker in real scene, and divide data set of 3D scene into segments by using region-growing segmentation. Then for each segment, detect 2D marker by extended RANSAC doing shape fitting. By this method, the 2D marker in 3D point cloud could be effectively detected without deforming or changing object. It provides simple and available features for the scene that is lack of features, laying a good foundation for next steps.