Generating 2D reflectance image from terrestrial laser scanner point cloud of different density and registration of point cloud

Terrestrial laser scanning has proven usability for many applications ranging from documentation of cultural heritage, vegetation analysis, mining, to three-dimensional (3D) reconstruction of architectural details and building facades. However, in all these applications, it is not possible to obtain a 3D model from a single scanning station. It is necessary to combine measurements from different scanning stations in a single reference system. This is still a problem for researchers, and it is desired to find solutions by developing new algorithms. Currently, the most preferred method for joining point clouds is the iterative nearest point method. Different from this method, many methods such as automatic feature detection algorithms or manual point cloud matching methods have been developed. One of these methods is the automatic or manual merging of point clouds using two-dimensional (2D) reflection images created by using the reflection values of the laser beam returning from the object surface and recorded in the scanner. The aim of this study is to combine reflection images and point clouds in cases where the geometric properties are insufficient and to investigate the effects of different scanning densities on this combination result. For this, scans with different spot frequencies of the 3D measurement area were made with terrestrial laser scanner (TLS). 2D reflection images were created from the obtained point clouds and point clouds were combined with these reflection images. The accuracy of the result obtained by binary fusion made from the reflection images was compared with the iterative closest point method, which is widely used in practice. In this thesis, pairwise merging of point clouds from reflection images is performed with an accuracy close to the iterative nearest point method, depending on the level of detail and complexity of the measured object and the scan point density.