Integration of Lidar and Photogrammetry for Close Range Applications

Various versions of LIDAR scanners (satellite-borne, airborne, and terrestrial) are perceived as fast and reliable technologies for direct acquisition of spatial data about the objects of interest. Compared to other measurement methods (e.g., photogrammetric manipulation of imagery), the equipment used for such scanners is still multi-part, bulky, and expensive. Moreover, ground-based LIDAR scanners require a stable platform where the system can be set up long enough for collecting the range data at the required resolution. Full coverage of close range objects requires adjacent scanning sessions (i.e., the LIDAR system should be moved around the object). However, there might be some restrictions with regard to setting up and/or operating the scanner (e.g., inaccessible and/or unstable scanning location). Hence it is not always possible to fully capture the object in question. In this regard, photogrammetry has the advantage of being able to quickly capture overlapping imagery with full coverage of the object to be mapped. Nonetheless, photogrammetric mapping is only possible providing that some control information is available. Establishing control information is not always possible (for the same reasons mentioned earlier; e.g., inaccessibility or instability). Since LIDAR provides scaled models of the object, the integration of LIDAR and photogrammetry would overcome the drawbacks of the individual systems. For example, one laser scan can be used to provide the required control information to establish the datum for the photogrammetric model. In the mean time, overlapping images would guarantee full coverage/mapping of the object under consideration. The only remaining problem is the identification of conjugate features in the LIDAR and photogrammetric models. It is extremely difficult, if not impossible, to identify conjugate points within the photogrammetric and LIDAR surface models. This paper outlines the use of control linear features derived from the LIDAR surface for establishing the datum for the photogrammetric model using corresponding linear features identified in the imagery. Experimental results using real data proved the feasibility of the suggested approach.