A novel method for locating the local terrestrial laser scans in a global aerial point cloud

In addition to the heterogeneity of aerial and terrestrial views, the small scale terrestrial point clouds are hardly comparable with large scale and overhead aerial point clouds. A hierarchical method is proposed for automatic locating of terrestrial scans in aerial point cloud. The proposed method begins with detecting the candidate positions for the deployment of the terrestrial laser scanner in the aerial point cloud. After that, by simulating the performance of the laser scanner, the visible portion of the aerial point cloud is detected and it is extracted as the candidate deployment aerial point cloud.  As a result, the problem of scan locating is converted to a corresponding one between several local terrestrial point clouds and several local aerial point clouds. In order to increase the comparability of these two datasets in the corresponding process, the main geometric structures of each point cloud are extracted using four predesigned geometric feature indexes, and they are organized in the form of four feature-maps of each point cloud. The feature-maps generated for each point cloud are described by the rotation invariant Fourier-HOG descriptor. Afterward, the corresponding problem is structured in the form of a k-nn classification among the classes established for these descriptors. Finally, the location of each terrestrial scan is obtained based on the classification results. The evaluation results of the proposed method on an urban dataset, showed an average accuracy of about 5 meters for locating the terrestrial scans in aerial point cloud. The obtained accuracies seem to be sufficient to enter the process of registering the terrestrial scans to the aerial point cloud.