Photogrammetric and Lidar Data Registration Using Linear Features

Abstract

The enormous increase in the volume of datasets acquired by lidar systems is leading towards their extensive exploitation in a variety of applications, such as, surface reconstruction, city modeling, and generation of perspective views. Though being a fairly new technology, lidar has been influenced by and had a significant impact on photogrammetry. Such an influence or impact can be attributed to the complementary nature of the information provided by the two systems. For example, photogrammetric processing of imagery produces accurate information regarding object space break lines (discontinuities). On the other hand, lidar provides accurate information describing homogeneous physical surfaces. Hence, it proves logical to combine data from the two sensors to arrive at a more robust and complete reconstruction of 3D objects. This paper introduces alternative approaches for the registration of data captured by photogrammetric and lidar systems to a common reference frame. The first approach incorporates lidar features as control for establishing the datum in the photogrammetric bundle adjustment. The second approach starts by manipulating the photogrammetric imagery to produce a 3D model, including a set of linear features along object space discontinuities, relative to an arbitrarily chosen coordinate system. Afterwards, conjugate photogrammetric and lidar straight-line features are used to establish the transformation between the arbitrarily chosen photogrammetric coordinate system and the lidar reference frame. The second approach (bundle adjustment, followed by similarity transformation) is general enough to be applied for the co-registration of multiple three-dimensional datasets regardless of their origin (e.g., adjacent lidar strips, surfaces in GIS databases, and temporal elevation data). The registration procedure would allow for the identification of inconsistencies between the surfaces in question. Such inconsistencies might arise from changes taking place within the object space or inaccurate calibration of the internal characteristics of the lidar and the photogrammetric systems. Therefore, the proposed methodology is useful for change detection and system calibration applications. Experimental results from aerial and terrestrial datasets proved the feasibility of the suggested methodologies.