• Korean Journal of Geomatics
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• v.5 no.1
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• pp.7-19
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• 2005

Photogrammetric mapping procedures have gone through major developments due to significant improvements in its underlying technologies. The availability of GPS/INS systems greatly assist in direct geo-referencing of the acquired imagery. Still, photogrammetric datasets taken without the aid of positioning and navigation systems need control information for the purpose of surface reconstruction. Point features were, and still are, the primary source of control for the photogrammetric triangulation although other higher-order features are available and can be used. LIDAR systems supply dense geometric surface information in the form of three dimensional coordinates with respect to certain reference system. Considering the accuracy improvement of LIDAR systems in the recent years, LIDAR data is considered a viable supply of photogrammetric control. To exploit LIDAR data, new challenges are poised concerning the representation and reference system by which both the photogrammetric and LIDAR datasets are described. In this paper, registration methodologies will be devised for the purpose of integrating the LIDAR data into the photogrammetric triangulation. Such registration methodologies have to deal with three issues: registration primitives, transformation parameters, and similarity measures. Two methodologies will be introduced that utilize straight-line and areal features derived from both datasets as the registration primitives. The first methodology directly incorporates the LIDAR lines as control information in the photogrammetric triangulation, while in the second methodology, LIDAR patches are used to produce and align the photogrammetric model. Also, camera self-calibration experiments were conducted on simulated and real data to test the feasibility of using LIDAR patches for this purpose.

• ETRI Journal
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• v.29 no.4
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• pp.411-420
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• 2007

The steady evolution of mapping technology is leading to an increasing availability of multi-sensory geo-spatial datasets, such as data acquired by single-head frame cameras, multi-head frame cameras, line cameras, and light detection and ranging systems, at a reasonable cost. The complementary nature of the data collected by these systems makes their integration to obtain a complete description of the object space. However, such integration is only possible after accurate co-registration of the collected data to a common reference frame. The registration can be carried out reliably through a triangulation procedure which considers the characteristics of the involved data. This paper introduces algorithms for a multi-primitive and multi-sensory triangulation environment, which is geared towards taking advantage of the complementary characteristics of spatial data available from the above mentioned sensors. The triangulation procedure ensures the alignment of involved data to a common reference frame. The devised methodologies are tested and proven efficient through experiments using real multi-sensory data.