Point cloud registration is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. In this paper, we present a novel probability driven algorithm for point cloud registration of the indoor scene based on RGB-D images. Firstly, we extract the key points in RGB-D images and map the key points to 3D space as preprocessing. Then, we build the distance matrix and the difference matrix for each point cloud, respectively in scalarization and vectorization, to encode the structural proximity. And establish the corresponding point set by computing the matching probabilities. At last, we solve the transform matrix that aligns the source point cloud to the target point cloud. The entire registration framework consists of two phases: coarse registration based on the distance matrix (in scalarization) and fine registration based on the difference matrix (in vectorization). The two-phase registration strategy is able to greatly reduce the influence of inherent noise. Experiments demonstrate that our method outperforms in registration accuracy than the state-of-the-art methods. Furthermore, our method is more efficient than existing methods in computing speed because we utilize the location relationship between key points instead of point features. The source code is provided at our project website https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene .

中文翻译：

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室内场景点云配准的概率驱动方法

点云配准是使用移动机器人或对象建模管道进行定位和映射的关键步骤。在本文中，我们提出了一种新的概率驱动算法，用于基于 RGB-D 图像的室内场景点云配准。首先，我们提取RGB-D图像中的关键点并将关键点映射到3D空间作为预处理。然后，我们分别在标化和矢量化中为每个点云构建距离矩阵和差异矩阵，以对结构邻近度进行编码。并通过计算匹配概率建立对应点集。最后，我们求解将源点云与目标点云对齐的变换矩阵。整个注册框架包括两个阶段：基于距离矩阵的粗配准（在标量化中）和基于差异矩阵的精细配准（在矢量化中）。两相配准策略能够大大降低固有噪声的影响。实验表明，我们的方法在配准精度方面优于最先进的方法。此外，我们的方法在计算速度方面比现有方法更有效，因为我们利用关键点之间的位置关系而不是点特征。源代码在我们的项目网站 https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene 上提供。实验表明，我们的方法在配准精度方面优于最先进的方法。此外，我们的方法在计算速度方面比现有方法更有效，因为我们利用关键点之间的位置关系而不是点特征。源代码在我们的项目网站 https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene 上提供。实验表明，我们的方法在配准精度方面优于最先进的方法。此外，我们的方法在计算速度方面比现有方法更有效，因为我们利用关键点之间的位置关系而不是点特征。源代码在我们的项目网站 https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene 上提供。