Initial Registration for Point Clouds Based on Linear Features

2011 ◽  
Author(s):  
Yunlan Guan ◽  
Hongjun Zhang
Keyword(s):  
Point Clouds ◽  
Linear Features

scholarly journals Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features

2018 ◽  
Vol 12 (3) ◽  
pp. 328-338 ◽  
Author(s):  
Hiroaki Date ◽  
Takahito Yokoyama ◽  
Satoshi Kanai ◽  
Yoshiro Hada ◽  
Manabu Nakao ◽  
...  
Keyword(s):  
Regular Point ◽  
Hash Table ◽  
Region Growing ◽  
Point Clouds ◽  
Horizontal Line ◽  
Concrete Bridge ◽  
Steel Bridge ◽  
Linear Features ◽  
Registration Method ◽  
Planar Regions

Efficient registration of point clouds from terrestrial laser scanners enables us to move from scanning to point cloud applications immediately. In this paper, a new efficient rough registration method of laser-scanned point clouds of bridges is proposed. Our method relies on straight-line edges as linear features, which often appear in many bridges. Efficient edge-line extraction and line-based registration methods are described in this paper. In our method, first, sampled regular point clouds based on the azimuth and elevation angles are created, and planar regions are extracted using the region growing on the regular point clouds. Then, straight lines of the edges of the planar regions are extracted as linear features. Next, vertical and horizontal line clusters are created according to the direction of the lines. To align the position and orientation of two point clouds, two corresponding nonparallel line pairs from line clusters are used. In the registration process, the RANSAC approach with a hash table of line pairs is used. In this process, the hash table is used for finding candidates of corresponding line pairs efficiently. Sampled points on the line pairs are used to align the line pairs, and occupied voxels and downsampled point clouds are used for efficient consensus calculation. The method is tested using three data sets of different types of bridges: a small steel bridge, a middle-size concrete bridge, and a high-pier concrete bridge. In our experiments, successful rates of our rough registration were 100%, and the processing time of rough registration for 19 point clouds was about 1 min.

scholarly journals ROBUST CLASSIFICATION AND SEGMENTATION OF PLANAR AND LINEAR FEATURES FOR CONSTRUCTION SITE PROGRESS MONITORING AND STRUCTURAL DIMENSION COMPLIANCE CONTROL

2015 ◽  
Vol II-3/W5 ◽  
pp. 129-136 ◽  
Author(s):  
R. Maalek ◽  
D. D. Lichti ◽  
J. Ruwanpura
Keyword(s):  
Moving Objects ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Construction Site ◽  
Structural Dimension ◽  
Construction Sites ◽  
Linear Features ◽  
Robust Clustering ◽  
Robust Classification ◽  
Building Information

The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.

scholarly journals A Closed-Form Solution to Linear Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 13 (18) ◽  
pp. 3571
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian ◽  
Hua Zhang
Keyword(s):  
Objective Function ◽  
Closed Form ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Linear Features ◽  
Registration Method ◽  
Linear Feature ◽  
Feature Based ◽  
Transformation Parameters

Due to the high complexity of geo-spatial entities and the limited field of view of LiDAR equipment, pairwise registration is a necessary step for integrating point clouds from neighbouring LiDAR stations. Considering that accurate extraction of point features is often difficult without the use of man-made reflectors, and the initial approximate values for the unknown transformation parameters must be estimated in advance to ensure the correct operation of those iterative methods, a closed-form solution to linear feature-based registration of point clouds is proposed in this study. Plücker coordinates are used to represent the linear features in three-dimensional space, whereas dual quaternions are employed to represent the spatial transformation. Based on the theory of least squares, an error norm (objective function) is first constructed by assuming that each pair of corresponding linear features is equivalent after registration. Then, by applying the extreme value analysis to the objective function, detailed derivations of the closed-form solution to the proposed linear feature-based registration method are given step by step. Finally, experimental tests are conducted on a real dataset. The derived experimental result demonstrates the feasibility of the proposed solution: By using eigenvalue decomposition to replace the linearization of the objective function, the proposed solution does not require any initial estimates of the unknown transformation parameters, which assures the stability of the registration method.

Position Estimation Using Crater-based Linear Features for Pinpoint Lunar Landing

2018 ◽  
Vol 17 (0) ◽  
pp. 79-87 ◽  
Author(s):  
Kazuki KARIYA ◽  
Takayuki ISHIDA ◽  
Shujiro SAWAI ◽  
Tomoo KINOSHITA ◽  
Kunihiro KAJIHARA ◽  
...  
Keyword(s):  
Position Estimation ◽  
Linear Features ◽  
Lunar Landing

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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