Optical Images and Terrestial Laser Scanning Co-registration by the Use of Feature Based Methodology

This paper focuses on the use of control linear features extraction from a terrestrial laser scanning (TLS) surface to produce a good datum definition for a close range photogrammetric model. The difficulty of identifying conjugate points between both datasets is overcome by the derivation of automatic features extraction. Hence, the extracted features from the terrestrial measurements represent a good candidate for co-registration. Field work is performed to validate the proposed methods. It combines a TLS acquisition on different city buildings, while a complete dataset of close range images of the same area is measured. Some additional geodetic survey control is added to the overall data to perform global registration. Those experiments are based on planar patches automatic recognition derived from segmentation techniques. Planar segmentation is the extraction of planes from a point cloud. Assuming that linear features are at the intersection of those planes, we are able to generate a solid photogrammetric datum. After identifying selected control points from the images dataset, a bundle adjustment is performed. The corresponding linear features are extracted manually from the photogrammetric dataset. The next consistent step is to calculate the similarity transformation between the TLS and the photogrammetric model using the lines previously identified. Results illustrate the good behavior of the proposed methodology in this particular type of study. This object based method is particularly interesting for the co-registration of photogrammetric images and points clouds coming from terrestrial LIDAR. Further studies should be conducted on the estimation of the normal distances between the lines and on the automation of these methods to produce 3D textured models.