The current point cloud registration methods are mainly based on geometric information and usually ignore the semantic information in the point clouds. In this paper, we treat the point cloud registration problem as semantic instance matching and registration task, and propose a deep semantic graph matching method for large-scale outdoor point cloud registration. Firstly, the semantic category labels of 3D point clouds are obtained by utilizing large-scale point cloud semantic segmentation network. The adjacent points with the same category labels are then clustered together by using Euclidean clustering algorithm to obtain the semantic instances. Secondly, the semantic adjacency graph is constructed based on the spatial adjacency relation of semantic instances. Three kinds of high-dimensional features including geometric shape features, semantic categorical features and spatial distribution features are learned through graph convolutional network, and enhanced based on attention mechanism. Thirdly, the semantic instance matching problem is modeled as an optimal transport problem, and solved through an optimal matching layer. Finally, according to the matched semantic instances, the geometric transformation matrix between two point clouds is first obtained by SVD algorithm and then refined by ICP algorithm. The experiments are cconducted on the KITTI Odometry dataset, and the average relative translation error and average relative rotation error of the proposed method are 6.6cm and 0.229{\deg} respectively.

翻译：当前的点云配准方法主要基于几何信息，通常忽略点云中的语义信息。本文将点云配准问题视为语义实例匹配与配准任务，提出一种用于大规模室外点云配准的深度语义图匹配方法。首先，利用大规模点云语义分割网络获取三维点云的语义类别标签；然后，通过欧氏聚类算法将具有相同类别标签的相邻点聚类，得到语义实例。其次，基于语义实例的空间邻接关系构建语义邻接图；通过图卷积网络学习几何形状特征、语义类别特征和空间分布特征三种高维特征，并基于注意力机制对其进行增强。再次，将语义实例匹配问题建模为最优传输问题，并通过最优匹配层求解。最后，根据匹配的语义实例，首先通过SVD算法获取两个点云间的几何变换矩阵，再通过ICP算法进行精化。在KITTI Odometry数据集上开展实验，本文方法的平均相对平移误差和平均相对旋转误差分别为6.6厘米和0.229°。