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Abstract
Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.
Highlights
Three-dimensional laser scanning is a technology that employs lasers to efficiently acquire spatial 3D data [1]
The efficient registration of point clouds, scanned from the urban scene, is a basic and vital requirement in point cloud processing, which is commonly employed in the point cloudbased simultaneous localization and mapping (SLAM) [6], positioning and navigation [7,8], 3D city reconstruction [9], and digital twins [10]
We used the farthest point sampling (FPS) algorithm to sample z points in each scan of point cloud as keypoints, and adopted the k-nearest neighbors (k-NN) algorithm to search k-nearest neighbors of each keypoint in the corresponding point cloud, so that each keypoint corresponds to a point set that is a local point patch, namely P1, . . . , Pz
Summary
Three-dimensional laser scanning is a technology that employs lasers to efficiently acquire spatial 3D data [1]. Features of the data, collected by laser scanning, include high precision, rich information, and real three-dimensionality, so it is widely used in urban planning [2], autonomous driving [3], high-precision mapping [4], and smart cities [5]. The urban scene is usually complex, and some objects have a high degree of similarity in the local structure. The spatial structure between different floors of the same. An automatic and efficient registration of TLS point clouds in the urban scene is a compelling and challenging task [11,12]
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