Automatic Registration of Terrestrial Laser Scanning Point Clouds using Panoramic Reflectance Images

This paper presents a new approach to the automatic registration of terrestrial laser scanning (TLS) point clouds using panoramic reflectance images. The approach follows a two-step procedure that includes both pair-wise registration and global registration. The pair-wise registration consists of image matching (pixel-to-pixel correspondence) and point cloud registration (point-to-point correspondence), as the correspondence between the image and the point cloud (pixel-to-point) is inherent to the reflectance images. False correspondences are removed by a geometric invariance check. The pixel-to-point correspondence and the computation of the rigid transformation parameters (RTPs) are integrated into an iterative process that allows for the pair-wise registration to be optimised. The global registration of all point clouds is obtained by a bundle adjustment using a circular self-closure constraint. Our approach is tested with both indoor and outdoor scenes acquired by a FARO LS 880 laser scanner with an angular resolution of 0.036° and 0.045°, respectively. The results show that the pair-wise and global registration accuracies are of millimetre and centimetre orders, respectively, and that the process is fully automatic and converges quickly

A Multi-View Extension of the ICP algorithm

ABSTRACT Although the Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration, it can only be applied to two point sets at a time. By only registering two scans at a time, ICP fails to exploit the redundant information available in multiple scans that have overlapping regions. In this paper, we present a multi-view extension of the ICP algorithm by a method that simultaneously averages the redundant information available in the scans with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilise the transitive property of point correspondences are also presented. The improved accuracy of this motion averaged approach in comparison with ICP and some multi-view methods is established through multiple tests. We also present results of our method applied to some well-known real datasets

YERSEL LAZER TARAYICI NOKTA BULUTLARININ BİRLEŞTİRİLMESİ VE JEODEZİK KOORDİNAT SİSTEMİNE DÖNÜŞTÜRÜLMESİ: LİTERATÜR ARAŞTIRMASI

YERSEL LAZER TARAYICI NOKTA BULUTLARININ BİRLEŞTİRİLMESİ VE JEODEZİK KOORDİNAT SİSTEMİNE DÖNÜŞTÜRÜLMESİ: LİTERATÜR ARAŞTIRMASIÖzetYersel lazer tarayıcılarla üç boyutlu (3B) modelleme çalışmalarında, nokta bulutlarının birleştirilmesi en önemli işlem adımlarından birisidir. Bu amaçla bugüne kadar pek çok yöntem geliştirilmiştir, ancak lazer tarayıcı nokta bulutlarının birleştirilmesi hala önemli bir araştırma konusudur. Diğer yandan nokta bulutlarının otomatik birleştirilmesi de ciddi bir araştırma konusudur ve her türlü veri seti için uygulanabilecek standart bir yöntem bulunmamaktadır. Uygulanan yöntemler; otomasyon, doğruluk, hesaplama süresi, nokta yoğunluğu ve ölçü hatalarına duyarlık bakımından farklılıklar göstermektedir. Ayrıca lazer tarama verilerinin başka konumsal verilerle entegrasyonu için jeodezik koordinat sistemi gibi ortak bir koordinat sistemine dönüştürülmesi gerekir. Ölçme tekniği ve lazer tarayıcı aletinin konfigürasyonuna bağlı olarak jeodezik koordinatlandırma yöntemleri değişiklik göstermektedir. Bu çalışmada lazer tarayıcı nokta bulutlarının birleştirilmesinde kullanılan yöntemler sınıflandırılmış ve belirli özellikleri vurgulanmıştır. Böylece nokta bulutlarının birleştirilmesi ve jeodezik koordinat sistemine dönüştürülmesi konusunda uygulayıcı ve araştırmacılara yol gösterici olunması amaçlanmıştır.Anahtar Kelimeler: Yersel lazer tarama, Nokta bulutu, Birleştirme, Üç boyutlu dönüşüm, LIDAR, Jeodezik koordinatlandırma.REGISTRATION AND GEOREFERENCING METHODS FOR POINT CLOUDS OF TERRESTRIAL LASER SCANNER: A REVIEWAbstractPoint cloud registration is bottle neck on three-dimensional (3B) modelling by using terrestrial laser scanner. Many methods have been developed for the registration of point clouds so far. Neverthless, it is still important research topic. Automatic registration of point clouds is also one of the important research topic on three-dimensional modelling. There are no standart methods for applying all type of data sets. The registration methods have different specicifications in respect to automation, accuracy, computation time, point density and susceptiblity from irregular points. On the other hand, the point clouds have to be registered into extensive coordinate system like this geodetic system for the integration with the other spatial data. Georeferencing methods of point clouds change according to measurement methods and configuration of laser scanner instrument. In this study, point cloud registration methods have been classified and emphasized their main properties. Thus, it had to be given informataion for the applicants and researchers about point cloud registration and georeferencing.Keywords: Terrestrial laser scanning, Point cloud, Alignment, Three-dimensional registration, LIDAR, Georeferencing

Fast Global Registration of 3D Sampled Surfaces Using a Multi-Z-Buffer Technique

We present a new method for the global registration of several overlapping 3D surfaces sampled on an object. The method is based on the ICP algorithm and on a segmentation of the sampled points in an optimized set of z-buffers. This multi-z-buffer technique provides a 3D space partitioning which greatly accelerates the search of the nearest neighbours in the establishment of the point-to-point correspondence between overlapping surfaces. Then a randomized iterative registration is processed on the surface set. We have tested an implementation of this technique on real sampled surfaces. It appears to be rapid, accurate and robust, especially in the case of highly curved objects. 1. Introduction A single scan of a complex 3D object is in general not sufficient to fully describe its surface because of the presence of occluded parts. Additional scans from different view points are thus required to recover the occluded parts and improve the surface description. To reach these different vie..