Registration is essential for terrestrial LiDAR (light detection and ranging) scanning point clouds. The registration of indoor point clouds is especially challenging due to the occlusion and self-similarity of indoor structures. This paper proposes a 4 degrees of freedom (4DOF) coarse registration method that fully takes advantage of the knowledge that the equipment is levelled or the inclination compensated for by a tilt sensor in data acquisition. The method decomposes the 4DOF registration problem into two parts: (1) horizontal alignment using ortho-projected images and (2) vertical alignment. The ortho-projected images are generated using points between the floor and ceiling, and the horizontal alignment is achieved by the matching of the source and target ortho-projected images using the 2D line features detected from them. The vertical alignment is achieved by making the height of the floor and ceiling in the source and target points equivalent. Two datasets, one with five stations and the other with 20 stations, were used to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved 80% and 63% successful registration rates (SRRs) in a simple scene and a challenging scene, respectively. The SRR in the simple scene is only lower than that of the keypoint-based four-point congruent set (K4PCS) method. The SRR in the challenging scene is better than all five comparison methods. Even though the proposed method still has some limitations, the proposed method provides an alternative to solve the indoor point cloud registration problem.

中文翻译：

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使用2D线特征对室内点云进行成对粗略配准

配准对于地面LiDAR（光检测和测距）扫描点云至关重要。由于室内结构的遮挡和自相似性，室内点云的配准尤其具有挑战性。本文提出了一种4自由度（4DOF）粗略配准方法，该方法充分利用了在数据采集中设备被调平或倾斜传感器补偿了倾斜度的知识。该方法将4DOF配准问题分解为两部分：（1）使用正投影图像的水平对齐和（2）垂直对齐。使用地板和天花板之间的点生成正投影图像，并通过使用从其中检测到的2D线特征匹配源正投影图像和目标正投影图像来实现水平对齐。通过使源点和目标点的地板和天花板的高度相等，可以实现垂直对齐。使用两个数据集（一个具有五个站点，另一个具有20个站点）来评估该方法的性能。实验结果表明，该方法在简单场景和具有挑战性的场景中分别实现了80％和63％的成功注册率（SRR）。简单场景中的SRR仅低于基于关键点的四点一致集（K4PCS）方法的SRR。具有挑战性的场景中的SRR优于所有五种比较方法。尽管所提出的方法仍然有一些局限性，但是所提出的方法为解决室内点云注册问题提供了一种替代方法。