A Broadly Applicable Coarse Alignment Framework for the Point Cloud of Hull Blocks

Abstract

The terrestrial laser scanning (TLS) technology, which has been widely used in recent years, provides a new approach to control the construction quality of hull blocks in shipyards. As the cloud-model registration problem plays the most significant role in the data processing stage of utilizing the TLS devices, this paper concentrates on developing a broadly applicable coarse alignment framework for the point cloud of hull blocks. This framework involves two steps: the recognition of the connection regions of the point clouds and the coarse registration method according to the classification results. To detect the connection regions automatically, a hand-crafted simple model of the connection regions of hull blocks is built and its supporting detection method is proposed, besides, considering the recent overwhelming success of deep learning methods, a deep learning network suitable for large hull block datasets is constructed according to the Point-Net, and convolutional neural networks paradigms. Then, the coarse alignment method based on the detected connection regions is proposed. Experimental results illustrate the good performance of the proposed framework. computers in construction; shipbuilding; automation