ORTHOGRAPHIC REFLECTANCE IMAGE FOR PLANAR TARGET LOCALIZATION IN LOW DENSITY TLS POINT CLOUDS
Point cloud registration is important and essential task for terrestrial laser scanning applications. Point clouds acquired at different positions exhibit significant variation in point density. Most registration methods implicitly assume dense and uniform distributed point clouds, which is hardly the case in large-scale surveying. The accuracy and robustness of feature extraction are greatly influenced by the point density, which undermines the feature-based registration methods. We show that the accuracy and robustness of target localization dramatically decline with decreasing point density. A methodology for localization of artificial planar targets in low density point clouds is presented. An orthographic image of the target is firstly generated and the potential position of the target center is interactively selected. Then the 3D position of the target center is estimated by a non-linear least squares adjustment. The presented methodology enables millimeter level accuracy of target localization in point clouds with 30mm sample interval. The robustness and effectiveness of the methodology is demonstrated by the experimental results.