Automatic registration of large-scale urban scene point clouds based on semantic feature points
Automatic registration of large-scale urban scene point clouds based on semantic feature points
复制标题
基于语义特征点的大规模城市场景点云自动配准
DOI：
10.1016/j.isprsjprs.2015.12.005
复制
发表时间：
2016-03-01
影响因子：
12.7
通讯作者：
Liu, Yuan
中科院分区：
文献类型：
--
作者：
Yang, Bisheng;Dong, Zhen;Liu, Yuan
地面激光扫描(TLS)从大尺度城市场景中采集的点云数据包含各种对称和不完全结构的物体(建筑物、汽车、杆状物体等)，以及相对低纹理的表面，这些都给扫描之间的自动配准带来了巨大的挑战。针对这些问题，本文提出了一种基于提取的语义特征点的无标记多视点配准方法。该方法首先对检测方案中的语义特征点进行检测，包括点云分割、垂直特征线提取和语义信息计算，最后将这些线与地面的交点作为语义特征点。其次，该方法使用几何约束(3点方案)和语义信息(类别和方向)来匹配语义特征点，从而在扫描之间进行穷举配准。最后，该方法通过构造由穷举配准得到的全连通图的最小生成树来实现多视图配准。实验表明，与基于特征平面的配准方法相比，该方法在不同的城市环境和室内场景下均能达到厘米级的配准精度，并提高了配准的效率、稳健性和精度。(C)2016年国际摄影测量和遥感学会(摄影测量和遥感学会)。爱思唯尔出版，版权所有。
Point clouds collected by terrestrial laser scanning (TLS) from large-scale urban scenes contain a wide variety of objects (buildings, cars, pole-like objects, and others) with symmetric and incomplete structures, and relatively low-textured surfaces, all of which pose great challenges for automatic registration between scans. To address the challenges, this paper proposes a registration method to provide marker free and multi-view registration based on the semantic feature points extracted. First, the method detects the semantic feature points within a detection scheme, which includes point cloud segmentation, vertical feature lines extraction and semantic information calculation and finally takes the intersections of these lines with the ground as the semantic feature points. Second, the proposed method matches the semantic feature points using geometrical constraints (3-point scheme) as well as semantic information (category and direction), resulting in exhaustive pairwise registration between scans. Finally, the proposed method implements multi-view registration by constructing a minimum spanning tree of the fully connected graph derived from exhaustive pairwise registration. Experiments have demonstrated that the proposed method performs well in various urban environments and indoor scenes with the accuracy at the centimeter level and improves the efficiency, robustness, and accuracy of registration in comparison with the feature plane-based methods. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.