The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss–Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.

An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point Clouds Registration

Measuring and generating a three-dimensional (3D) model using laser scanning techniques is increasingly common in various fields because laser scanners can produce a large number of observation points in a short time. This study focuses on data acquisition using Leica C10 laser scanner and 3D modeling using Autodesk Revit software for construction industry and that which is in accordance to QLASSIC standards. Leica C10 is known as a long distance laser scanner that is suitable for collecting data of large objects while Autodesk Revit is a software for generating 3D models using laser scanner data for construction industry. Two building structures namely precast concrete and cast-in-situ concrete were used in this study. The crucial procedure before data collection was to ensure that the station of laser scanner allowed at least three black/white targets to be viewed for registration purposes. For the analysis, the distance measured between design model and measuring tape, and distance measured between design model and 3D model from the laser scanner were compared. To support QLASSIC, the difference should not exceed ± 10mm. The results of the study for the precast concrete show that the value of RMSE between the design model and the 3D model from the laser scanner is 2.972mm while for the design model and the measuring tape is 14mm. For cast-in-situ concrete, the Root Mean Square Error (RMSE) value between the design model and the 3D model from the laser scanner is 3.346mm while the RMSE value between the design model and the measuring tape is 14.823mm. The results of the analysis indicate that the measured distance between the design model and the 3D model from the laser scanner is in accordance with the permissible accuracy in QLASSIC standard. The flatness percentage analysis was also performed for cast-in-situ concrete. While the QLASSIC standard for flatness percentage analysis is set at 70%, the flatness percentage analysis for cast-in-situ concrete between design model and the 3D model from the laser scanner is 79.5%. In conclusion, Leica C10 is suitable for industrial construction and supports QLASSIC standards.

Dimensional survey using terrestrial laser scanning for industrial construction in Malaysia

The purpose of this study was for determine the application Terrestrial Laser Scanner (TLS) in the as-built model buildings in three dimensions based on the Level of Detail 3 (LOD3). Analysis on the measurement of the dimensions of the building was carried out to determine the accuracy of the final results obtained. The aim of this study is to generate a 3D model to help with asbuilt work, construction monitoring, and for the other user. TLS is used for data acquisition in this study is to achieve the measurement accuracy and more efficient. In addition to reducing hazardous and safety issues that occur in the field. The usual data acquisition methods needs labor intensive and it is time consuming. 3D Model as built building is resulting in Level of Detail 3 (LOD3), can be used for the management of construction, during operation and also in the maintenance phase related to engineering work. With the resulting model, this may increase work efficiency and provide motivation to the person in charge to ensure that work is carried out in accordance with the requirements of employers. Typically the two-dimensional (2D) as-built plans or data is very popular, but in fact it is not so efficient and is less friendly to be used by project owners, landlords and others to review the status of development projects and to see the changes that occur in the construction system. Based on the final results, the model has met the conditions set by CityGML, which is 0.5m accuracy for 3D model in the LOD3. Furthermore, the use of 'plug-ins' were proposed are necessary to enable the switch point cloud data to be processed in the modeling phase of the software modeling. In addition, it is also useful to reduce the size of raw data without reducing the quality of the resulting data.

Application of terrestrial laser scanner for three dimensional as built building model

Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of co-registration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In the literature, one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D least squares (LS) matching methods as well. In most of the co-registration methods, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values. This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a new method where the stochastic properties of both (template and search) surfaces are considered under an errors-in-variables (EIV) model. The experiments have been carried out using a close range laser scanning data set and the results of the conventional and EIV types of the ICP matching methods have been compared.

/pdf/co-registration-of-3d-point-clouds-by-using-an-errors-in-10bezu08z1.pdf

Co-registration of 3d point clouds by using an errors-in-variables model

. Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of coregistration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In computer vision and photogrammetry domain one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D Least Squares (LS) matching methods as well (Gruen and Akca, 2005). The co-registration methods commonly use the least squares (LS) estimation method in which the unknown transformation parameters of the (floating) search surface is functionally related to the observation of the (fixed) template surface. Here, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values of the search surface. . This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a method where the stochastic properties of both the observations and the parameters are considered under an errors-in-variables (EIV) model. The experiments have been carried out using diverse laser scanning data sets and the results of EIV with the ICP and the conventional LS matching methods have been compared.

/pdf/total-least-squares-registration-of-3d-surfaces-2z8ehndeey.pdf

U. Aydar

Papers

Co-registration of 3d point clouds by using an errors-in-variables model

TOTAL LEAST SQUARES REGISTRATION of 3D SURFACES