Tsai Chi-Yi, Huang Chih-Hung
Department of Electrical and Computer Engineering, TamKang University, 151 Yingzhuan Road, Tamsui District, New Taipei City 251, Taiwan.
Sensors (Basel). 2017 Aug 15;17(8):1874. doi: 10.3390/s17081874.
With the increasing popularity of RGB-depth (RGB-D) sensor, research on the use of RGB-D sensors to reconstruct three-dimensional (3D) indoor scenes has gained more and more attention. In this paper, an automatic point cloud registration algorithm is proposed to efficiently handle the task of 3D indoor scene reconstruction using pan-tilt platforms on a fixed position. The proposed algorithm aims to align multiple point clouds using extrinsic parameters of the RGB-D camera obtained from every preset pan-tilt control point. A computationally efficient global registration method is proposed based on transformation matrices formed by the offline calibrated extrinsic parameters. Then, a local registration method, which is an optional operation in the proposed algorithm, is employed to refine the preliminary alignment result. Experimental results validate the quality and computational efficiency of the proposed point cloud alignment algorithm by comparing it with two state-of-the-art methods.
随着RGB深度（RGB-D）传感器越来越普及，利用RGB-D传感器重建三维（3D）室内场景的研究受到了越来越多的关注。本文提出了一种自动点云配准算法，以有效处理在固定位置使用云台平台进行3D室内场景重建的任务。该算法旨在利用从每个预设云台控制点获得的RGB-D相机的外部参数来对齐多个点云。基于离线校准的外部参数形成的变换矩阵，提出了一种计算效率高的全局配准方法。然后，采用一种局部配准方法（该方法是所提算法中的一个可选操作）来细化初步对齐结果。通过与两种最先进的方法进行比较，实验结果验证了所提点云对齐算法的质量和计算效率。
