Target-free Automatic Point Clouds Registration Using 2D images (original) (raw)

Target-Free Automatic Registration of Point Clouds

International Symposium on Automation and Robotics in Construction (ISARC), 2016

– The aim of this paper is to introduce a novel method that automatically registers colored 3D point cloud sets without using targets or any other manual alignment processes. For fully automated point cloud registration without targets or landmarks, our approach utilizes feature detection algorithms used in computer vision. A digital camera and a laser scanner is utilized and the sensor data is merged based on a kinematic solution. The proposed approach is to detect and extract common features not directly from a 3D point cloud but from digital images corresponding to the point clouds. The initial alignment is achieved by matching common SURF features from corresponding digital images. Further alignment is obtained using plane segmentation and matching from the 3D point clouds. The test outcomes show promising results in terms of registration accuracy and processing time.

An Automatic Robust Point Cloud Registration on Construction Sites

International Workshop on Computing for Civil Engineering (IWCCE), 2017

Point cloud-based reconstruction is a crucial process for process monitoring and as-built modeling, but still a challenging task in construction applications. Due to the finite perspective of a scan, multiple scans from different locations are required to obtain full scene coverage. Then, a registration process is required to transform all point clouds into a common coordinate frame. In this paper, a fully automated target-less point cloud registration method is presented. For this approach, a laser scanning device and a co-registered digital camera are required. This method consists of three major steps for an automatic registration of point clouds. First, it detects common features on the corresponding overlapped images from different locations. Second, it achieves coarse registration using the extracted features on images. Lastly, the fine registration is attained by the iterative closet point (ICP) algorithm. The proposed novel method was tested on a real construction site and the test results of automatic registration of all different scan positions were successfully verified in terms of accuracy. INTRODUCTION Both laser scanning and photogrammetry have been highly studied with the recent improvement in sensing technologies. These two technologies have strengths and weaknesses based on working environments and data quality requirements; however, photogrammetry cannot provide the same level of accuracy as laser scanners do in general. In particular, 3D laser scanning technology has been widely used in construction applications to render as-built objects or environments in the form of dense point cloud data. The abundant amount of point cloud data can be used to efficiently model a construction site. A complete point cloud data of the construction site without shading area can be acquired by scanning multiple times in different scanning positions. Each scan position defines a local coordinate system, which means that the point cloud of each viewpoint is referred to this local frame. A common reference system is required to analyze different laser scanning positions. The transformation process of all local coordinate systems into a common reference coordinate system is called registration. Many studies have been conducted on the registration of multi-view point clouds in the past few years. There are several types of point cloud registration have been studied; target-based, ICP-based, and feature-based. Target-based registration is a reliable and precise method, but is time-consuming. Before scanning, the targets must be placed inside the scanning area. After scanning, the targets have to be collected and manually or automatically recognized to define corresponding points for the registration. Becerik-gerber et al. (2011) proposed a target-based point cloud registration method. They experimented with three different types of targets such as fixed paper, paddle, and sphere, and with

FULL AUTOMATIC REGISTRATION OF LASER SCANNER POINT CLOUDS

In: Gruen, A., Kahmen, H. (Eds.), Optical 3-D Measurement Techniques VI, Zurich, Switzerland, September 22-25, vol. I, pp. 330-337., 2003

ABSTRACT The registration of point clouds that are acquired from different laser scanner standpoints is an essential task in the environment modelling works. In this paper, a full automatic point cloud registration scheme is presented. Special targets attached onto the object(s) are used as landmarks and their 3-D coordinates are measured with a theodolite in a ground coordinate system before the scanning process. The presented registration scheme can automatically find these targets in the point clouds using radiometric and geometric information (shape, size, and planarity). At the last step, targets are labelled using the consistent labelling by discrete relaxation in order to find the actual names of the points in the ground control points list. KEY WORDS : Laser Scanning, Point Cloud, Registration, Consistent Labeling, 3-D Similarity Transformation

Automated Point Clouds Registration using Visual and Planar Features for Construction Environments

ASCE Journal of Computing in Civil Engineering, 2017

Due to the limited view of each single laser scan data, multiple scans are required to cover all scenes of the large construction site, and a registration process is needed to merge them together. While many research efforts have been made on the automatic point cloud registration, however the prior works have some limitations; the automatic registration was tested in a bounded region and required a large overlapped area between scans. The aim of this paper is to introduce a novel method that achieves the automatic point cloud registration in an unbounded region and with a relatively small overlapped area without using artificial targets, landmarks, or any other manual alignment process. For the automatic point cloud registration, the proposed framework utilizes the

AUTOMATIC REGISTRATION OF LASER SCANNED COLOR POINT CLOUDS BASED ON COMMON FEATURE EXTRACTION

16th International Conference on Construction Applications of Virtual Reality (CONVR), 2016

Point cloud data acquisition with laser scanners provides an effective way for 3D as-built modeling of a construction site. Due to the limited view of a scan, multiple scans are required to cover the whole scene, and a registration process is needed to merge them together. The aim of this paper is to introduce a novel method that automatically registers colored 3D point cloud sets without using targets or any other manual alignment processes. For fully automated point cloud registration without artificial targets or landmarks, this study uses 1) the SpeededUp Robust Features (SURF) algorithm to identify geometric features among the series of scans and 2) plane-toplane matching algorithm to achieve precise registration. For an initial alignment during the registration process, common feature extraction is utilized to perform a 3D rigid-body transformation followed by aligning the view into the reference system. Further alignment is obtained using plane segmentation and matching from the 3D point clouds. The test outcomes show that the method is able to achieve registration accuracy of less than 1 o in deviation angle.

Point Cloud Registration Based on Image Correspondences

International Journal of Heritage in the Digital Era, 2013

Since the early '80s, when Digital Photogrammetry was in its infancy, important progress has been made in the area of automations using the most common photogrammetric procedures. The main objective of this paper is the full automation of 3D Point Cloud Registration process. In particular, an alternative registration method is being presented, based on corresponding points, which are detected on overlapping images that come with each scan using a structured light scanner. The algorithm detects corresponding points applying Feature Based Matching techniques. These are then interpolated directly to any given texture map, thereby the transition to correspondent 3D vertices is achieved. Finally, the algorithm computes the 3D Rigid Body transformation, which is applied to the 3D point clouds, in order to transform one scan's reference system into the other. Experimental results obtained by the proposed method are presented, evaluated and compared with those obtained by standard ICP implementation.

An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration

ISPRS International Journal of Geo-Information

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by ...

A novel approach to automatic registration of point clouds

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007

For the 3D reconstruction inside historic buildings, we need a marker-free automatic registration approach to align different views together, because GPS does not work indoors and markers are not allowed to paste on the walls. This paper presents an automatic matching process, which employs a novel algorithm, Dynamic Matching Tree technique, for a fast and stable coarse-matching to achieve the automatic pre-alignment of two point clouds and uses modified ICP to do a fine matching efficiently. The whole process can be divided in the following stages: preprocessing, 2-View matching and N-View matching. To validate our method, various experiments has been done on reconstruction of historic sites and industrial objects.

Full Automatic Registration of Laser Scanner Point Clouds, Optical 3D

2003

The registration of point clouds that are acquired from different laser scanner standpoints is an essential task in the environment modelling works. In this paper, a full automatic point cloud registration scheme is presented. Special targets attached onto the object(s) are used as landmarks and their 3-D coordinates are measured with a theodolite in a ground coordinate system before the scanning process. The presented registration scheme can automatically find these targets in the point clouds using radiometric and geometric information (shape, size, and planarity). At the last step, targets are labelled using the consistent labelling by discrete relaxation in order to find the actual names of the points in the ground control points list. 1

Point Cloud Registration and Accuracy for 3D Modelling - a Review

Journal of Information System and Technology Management, 2021

Geoinformation is a surveying and mapping field where topography and details on the ground are spatially mapped. The point cloud is one of the data types that is used for measurement and visualisation of Earth features mapping. Point cloud could come from a different source such as terrestrial laser scanned or photogrammetry. The concepts of terrestrial laser scanning and photogrammetry surveying are elaborated in this paper. This paper also presents the method used for point cloud registration; Iterative Closest Point (ICP) and Feature Extraction and Matching (FEM) and the accuracy of laser scanned, and photogrammetric point cloud based on the previous experiments. Experimental analysis conducted in the previous study shows an impressive result on laser scanned point cloud with very mínimum errors compared to photogrammetric point cloud.