期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2007
卷号：XXXVI-5/C55
出版社：Copernicus Publications
摘要：The ever improving capabilities of the direct geo-referencing technology is having a positive impact on the widespread adoption of LIDAR systems for the acquisition of dense and accurate surface models over extended areas. Unlike photogrammetric techniques, derived footprints from a LIDAR system are not based on redundant measurements, which are manipulated in an adjustment procedure. The accuracy of derived LIDAR footprints depends on the quality of the bore-sighting parameters among the system components: namely, the laser, GPS, and INS units. Current methodologies for estimating the bore-sighting parameters of a LIDAR system are based on complicated and sequential calibration procedures. This paper presents a new methodology for estimating the LIDAR bore-sighting parameters through a tight integration procedure that involves photogrammetric data and raw measurements from a LIDAR system. Then, the LIDAR bore-sighting parameters are determined by minimizing the normal distances between the derived LIDAR footprints and the photogrammetric patches. The whole procedure is implemented in an integrated bundle adjustment that incorporates the photogrammetric data as well as the raw LIDAR measurements. An analysis will be conducted to determine the optimum configuration of the control patches as well as the flight pattern for reliable estimation of the bore-sighting parameters while avoiding possible correlations. Besides the estimation of the bore-sighting parameters, the proposed methodology will also ensure the co-registration of the photogrammetric and LIDAR data to a common reference frame, which will have a positive impact on further products such as orthophotos and generated photo-realistic 3D models. The findings of the conducted analysis will be verified through experimental results from simulated data
关键词：Quality Assurance; System Biases; Linear Features; Areal Features; Surface Matching
