Photogrammetric and lidar data integration using the centroid of a rectangular roof as a control point

Abstract

The integration of photogrammetric images and lidar data is becoming a powerful procedure that can be applied in the optimisation of photogrammetric mapping techniques. The complementary nature of lidar and photogrammetric data optimises the performance of many procedures used to extract 3D spatial information from data. For example, photogrammetric imagery enables the accurate extraction of building borders and lidar provides accurate 3D points that give information on the physical surfaces of buildings. These properties demonstrate the usefulness of combining the two types of data to achieve a more robust and complete reconstruction of 3D objects. Photogrammetric procedures require the exterior orientation parameters (EOPs) of the images to extract mapping information. Despite the availability of GPS ⁄ INS systems, which greatly assist in direct georeferencing of the imagery, the majority of commercially available photogrammetric systems require control information in order to carry out photogrammetric mapping. Due to improvements in the accuracy of lidar systems in recent years, lidar data is considered a viable source of photogrammetric control. Point features are the principal source of control for photogrammetric triangulation, although linear features and planar patches have also been used. This paper presents a method of georeferencing photogrammetric images using lidar data. The method uses the centroids of rectangular building roofs as control points in the photogrammetric procedure. The centroid of a rectangular building roof derived using lidar data is equivalent to a single control point with 3D coordinates, and can therefore be used in traditional photogrammetric systems. Two photogrammetric experiments were carried out to verify the feasibility of the methodology. The results obtained from these experiments confirm the feasibility of applying the proposed methodology to the georeferencing of photogrammetric images using lidar data.

STUDY OF THE INTEGRATION OF LIDAR AND PHOTOGRAMMETRIC DATASETS BY IN SITU CAMERA CALIBRATION AND INTEGRATED SENSOR ORIENTATION

“…According to Vosselman and Maas (2010), the horizontal position accuracy of a control points, extracted from Lidar point cloud, can vary from 0.1 to 1.0 meters. For the expected vertical accuracy it was considered the vertical accuracy of the 3-D photogrammetric intersection, estimated based on the average flight height (1.600 m), the average baseline (508 m), the image measurement precision (0.003 mm), and the focal length (51.7 mm).…”

“…For this approach, methods to extract geometric primitives, such as points, lines and areas, are required because the Lidar point cloud does not directly mapping these geometric features. Several studies were conducted on this thematic, such as Habib et al, 2004;Delara et al, 2004;Habib et al, 2005;Mitishita et al, 2008;Wildan et al, 2011. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. This procedure can automatically acquire the LIDAR and imagery data sets in the same geodetic or mapping frame.…”

The Influence of Sub-Block Position on Performing Integrated Sensor Orientation Using In Situ Camera Calibration and Lidar Control Points

“…Csanyi and Toth [5] conducted research to develop an optimal ground-control target to improve the accuracy of Lidar data in mapping projects. Mitishita et al [6] proposed an approach to extract the centroid of building roofs from Lidar point clouds. In [7], LCPs were used in the aerial triangulation process of a large photogrammetric the experiments.…”

Relative orientation between a single frame image and LIDAR point cloud using linear features

“…Schenk and Csathó, 2002;Schenk, 2004;Habib et al, 2005a;Liu and Stamon, 2007;Wu, 2009;Choi et al, 2011;Chen and Lo, 2012) has been relatively popular. Other approaches have been, for example, use of roof centroids (Mitishita et al, 2008) and the general shape of objects in the scene (Rönnholm et al, 2003). An overview of the methodologies used in the fusion of lidar data and aerial imagery has been given for example in Schenk and Csathó (2002), who also emphasized the role of the straight lines in their fusion problem.…”