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Photogrammetric and Lidar Data Registration Using Linear Features

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Photogrammetric and Lidar Data Registration Using Linear Features 03-071.qxd 9/5/05 21:05 Page 699 Photogrammetric and Lidar Data Registration Using Linear Features Ayman Habib, Mwafag Ghanma, Michel Morgan, and Rami Al-Ruzouq Abstract objects in which surfaces play an important role (Habib and The enormous increase in the volume of datasets acquired Schenk, 1999). Photogrammetry is the conventional method by lidar systems is leading towards their extensive exploita- for surface reconstruction. However, lidar systems, whether tion in a variety of applications, such as, surface reconstruc- ground based, airborne, or space borne, have recently emerged tion, city modeling, and generation of perspective views. as a new technology with a promising potential towards Though being a fairly new technology, lidar has been influ- dense and accurate data capture on physical surfaces (Schenk enced by and had a significant impact on photogrammetry. and Csathó, 2002). Photogrammetry and lidar have unique Such an influence or impact can be attributed to the com- characteristics that make either technology preferable in plementary nature of the information provided by the two specific applications. For example, photogrammetry is more systems. For example, photogrammetric processing of suited for mapping heavily populated areas, while lidar is imagery produces accurate information regarding object preferable in mapping Polar Regions. However, one can space break lines (discontinuities). On the other hand, lidar observe that a negative aspect in one technology is con- provides accurate information describing homogeneous trasted by a complementary strength in the other. Therefore, physical surfaces. Hence, it proves logical to combine data integrating the two systems would prove extremely benefi- from the two sensors to arrive at a more robust and com- cial (Schenk and Csathó, 2002). plete reconstruction of 3D objects. This paper introduces Photogrammetric object space reconstruction starts with alternative approaches for the registration of data captured identifying features of interest in overlapping images. Con- by photogrammetric and lidar systems to a common refer- jugate features and the exterior orientation parameters of the ence frame. The first approach incorporates lidar features as involved images are then used in an intersection procedure control for establishing the datum in the photogrammetric yielding corresponding object features. Surfaces derived from bundle adjustment. The second approach starts by manipu- terrestrial and aerial imagery possess a rich body of scene lating the photogrammetric imagery to produce a 3D model, information. Moreover, derived object space features are very including a set of linear features along object space disconti- accurate, especially if they appear in more than two images as nuities, relative to an arbitrarily chosen coordinate system. a result of the high redundancy. The weakness of photogram- Afterwards, conjugate photogrammetric and lidar straight- metry is the “matching problem” (i.e., finding corresponding line features are used to establish the transformation be- features in overlapping images). The success of automatic tween the arbitrarily chosen photogrammetric coordinate surface reconstruction from imagery is contingent on the system and the lidar reference frame. The second approach reliability of the matching process. Manual or automatic (bundle adjustment, followed by similarity transformation) is matching is only possible when features with unique gray- general enough to be applied for the co-registration of mul- scale value distribution function are used. As a result, im- tiple three-dimensional datasets regardless of their origin plemented features usually correspond to locations along (e.g., adjacent lidar strips, surfaces in GIS databases, and discontinuities in one or more directions within the images temporal elevation data). The registration procedure would (e.g., edges and interest points). Such features usually pertain allow for the identification of inconsistencies between the to discontinuities and break lines in the object space. There- surfaces in question. Such inconsistencies might arise from fore, photogrammetric surfaces provide a rich set of informa- changes taking place within the object space or inaccurate tion along object space break lines and almost no information calibration of the internal characteristics of the lidar and the along homogeneous surfaces with uniform texture. photogrammetric systems. Therefore, the proposed method- Lidar has been conceived as a method to directly and ology is useful for change detection and system calibration accurately capture digital elevation data. However, in order applications. Experimental results from aerial and terrestrial to reach the high accuracy potential, the lidar system must datasets proved the feasibility of the suggested methodologies. be well calibrated and equipped with a high end DGPS/INS navigation unit (Filin and Csathó, 1999). An appealing feature in the lidar output is the direct availability of 3D Introduction coordinates of points in the object space. The surface recon- Currently, a variety of applications demand fast and reliable struction process is simply formulated as a three-dimensional collection of data about physical objects (e.g., automatic DEM rigid body transformation of points from scanner space to generation, city modeling, and object recognition). Such object space. One should note that there is no inherent applications require the availability of information pertain- redundancy in the computation of lidar points. Moreover, ing to the geometric and semantic characteristics of such Photogrammetric Engineering & Remote Sensing Vol. 71, No. 6, June 2005, pp. 699–707. Department of Geomatics Engineering, University of Calgary, 2500, University Drive, NW., Calgary, Alberta, Canada T2N 0099-1112/05/7106–0699/$3.00/0 1N4 ([email protected]; mghanma@geomatics. © 2005 American Society for Photogrammetry ucalgary.ca; [email protected]; [email protected]). and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING June 2005 699 03-071.qxd 9/5/05 21:05 Page 700 lidar surfaces are mainly positional, and there is no addi- registration primitives from photogrammetric and lidar data tional semantic or scene information available except when is explained. Then, the details of the mathematical model for the intensity of the reflected signal is recorded. Since lidar establishing the transformation parameters between the two provides a discrete set of irregularly distributed object points, datasets are introduced. The last two sections cover experi- the acquired surfaces possess rich information along homo- mental results (using terrestrial and airborne datasets), as geneous physical surfaces, and almost no information along well as, conclusions and recommendations for future work. object space discontinuities. It should be clear from the previous discussion that the integration of photogrammetric and lidar data would be Registration Paradigm extremely beneficial. For example, lidar surfaces could be In general, any registration process aims at combining data used to constrain and resolve ambiguities in the photogram- and information from multiple sensors in order to achieve an metric matching process. Moreover, photogrammetric data improved accuracy and better inference about the environ- will enrich lidar surfaces by providing more semantic attri- ment than could be attained through the use of a single butes. Also, photogrammetric and lidar surfaces can be sensor. Due to the enormous increase in the volume of spa- inspected for inconsistencies, which has to be justified (e.g., tial data that is being acquired by an ever-growing number changes in the object space or inaccurate calibration of the of sensors, there is a pressing need for the development of internal characteristics of either system). Therefore, success- accurate and robust registration procedures that can handle ful integration will facilitate subsequent processing activities spatial data with varying formats. An effective registration such as system calibration, object recognition, and genera- procedure must address the following issues (Brown, 1992): tion of 3D textured models. However, achieving the full potential of the synergism between the two technologies is Registration Primitives contingent on accurate and reliable co-registration of the The first step in the registration procedure is to decide upon respective surfaces relative to the same reference frame the primitives to use for establishing the transformation be- (Habib and Schenk, 1999; Schenk, 1999; Postolov et al., tween the datasets in question. The primitive choice influ- 1999). This should not be surprising, since any registration ences subsequent registration steps. In this research, straight- process aims at combining data and information from mul- line features have been used as the registration primitives. tiple sensors in order to achieve improved accuracies and This choice is motivated by the fact that such primitives can better inference about the environment than could be attained be reliably, accurately, and automatically extracted from through the use of a single sensor (Brown, 1992). photogrammetric and lidar datasets. The most common methods for solving the registration problem between two datasets are based on the identifica- Similarity Measure tion of common points. Such methods are not applicable The next step in the registration paradigm is the selection of when dealing with lidar surfaces, since
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