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The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation orurban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systemsby capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinateframe. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projectivescan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratioof inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. Thisconsistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with areasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset andshow that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers

GEOMETRIC POINT QUALITY ASSESSMENT FOR THE AUTOMATED, MARKERLESS AND ROBUST REGISTRATION OF UNORDERED TLS POINT CLOUDS