点云配准是基于RGB-D(RGB-depth)传感器的室内场景重建的关键技术之一。针对稀疏建图中关键帧间的点云配准问题,提出一种基于改进的随机采样一致性(RANSAC)的场景分类点云粗配准算法。首先分别利用几何信息与光度信息进行关键点的检测、描述与匹配,然后由场景分类算法判断场景类别,适应性地结合几何匹配与光度匹配,最后提出一种改进的RANSAC算法,通过有偏重的随机采样与自适应的假设评价,对关键帧间的变换矩阵进行估计。采用公开的RGB-D数据集对整体的点云粗配准算法进行实验验证,并与多种算法进行比较。实验结果表明,该点云粗配准算法能够实现稳健有效的变换矩阵估计,有助于后续的精配准与整体的室内场景重建。

Point cloud registration is one of the key technologies for indoor scene reconstruction based on the RGB-D(RGB-depth) sensor. To solve the point cloud registration problem among key frames in sparse mapping, this study proposes a coarse registration algorithm with scene classification based on improved random sample consensus (RANSAC). First, geometric information and photometric information are used to detect, describe, and match keypoints. Then, the scene classification algorithm determines the scene category, and geometric and photometric correspondences are adaptively combined. Finally, the improved RANSAC is proposed to estimate the transformation among key frames by biased random sampling and adaptive hypothesis evaluation. The whole coarse registration algorithm is experimentally verified by the public RGB-D dataset and compared with several algorithms. Experimental results show that the coarse registration algorithm can achieve robust and effective transformation estimation, which is helpful for subsequent fine registration and overall indoor scene reconstruction.