Assessment and comparison of registration algorithms between aerial images and laser point clouds

Pothou, A; Karamitsos, S; Georgopoulos, A; Kotsis, I

dc.contributor.author	Pothou, A	en
dc.contributor.author	Karamitsos, S	en
dc.contributor.author	Georgopoulos, A	en
dc.contributor.author	Kotsis, I	en
dc.date.accessioned	2014-03-01T01:55:22Z
dc.date.available	2014-03-01T01:55:22Z
dc.date.issued	2006	en
dc.identifier.issn	17689791	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27699
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-34948905254&partnerID=40&md5=f66e6eaeffdb964fb796063894663b8f	en
dc.subject	DEM matching	en
dc.subject	Fusion	en
dc.subject	Laser point cloud	en
dc.subject	LiDAR	en
dc.subject	Photogrammetry	en
dc.subject	Registration	en
dc.subject.other	Algorithms	en
dc.subject.other	Data acquisition	en
dc.subject.other	Optical radar	en
dc.subject.other	Photogrammetry	en
dc.subject.other	Problem solving	en
dc.subject.other	DEM matching	en
dc.subject.other	Laser point cloud	en
dc.subject.other	Image registration	en
dc.title	Assessment and comparison of registration algorithms between aerial images and laser point clouds	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	Photogrammetry, has been providing accurate coordinate measurements through the stereoscopic method for many years. LiDAR on the other hand, is becoming the prime method for large-scale acquisition of elevation data due to its capability of directly measuring 3D coordinates of a huge number of points. LiDAR can provide measurements in areas where traditional photogrammetric techniques encounter problems mainly due to occlusions or shadows. However LiDAR has also its limitations due to its inability of thematic information recording. The aim of this research is the optimum exploitation of both these elevation data sources. Usually this process is referred to as fusion of two datasets and exploits the advantages of both sources. The prerequisite step for the fusion of the two datasets is the co-registration. This paper describes a co-registration procedure, between the two datasets, that takes place through a 3D transformation. The performance of the algorithm A, which was presented in an earlier publication by the authors, and also an extended algorithm B based on the inclusion of the 7 transformation parameters, during the whole registration procedure, have been presented. Both algorithms developed based on the minimization of the distances between points of one surface to surface patches of the other surface, parallel to the corresponding surface normals. A comparison has also been performed between the results of the algorithms developed, in order to assess the geometric stability of the transformations, the analysis of the results, the effects on the registration and the accuracy of the derived parameters through stand-alone macros. Due to improved geometry of extended algorithm B an increased performance was expected. In fact the algorithm B converges 50% faster than the algorithm A. To support these tests a block of aerial images and a one month apart 3D laser point cloud were collected.	en
heal.journalName	Revue Francaise de Photogrammetrie et de Teledetection	en
dc.identifier.issue	182	en
dc.identifier.spage	28	en
dc.identifier.epage	33	en