Registration of Laser Scanning Point Clouds: A Review

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, H.; Wu, Yang; Li, Manchun; Chen, Yanming

doi:10.3390/s18051641

Cited by 214 publications

(139 citation statements)

References 162 publications

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“…The TLS data orientation process is the first and most important stage of TLS data processing; it involves aligning point clouds in the assumed reference system, which may be the stated coordinate system, a local system, or an internal system related to one of the scans, the so-called reference scan [19].…”

Section: Related Workmentioning

confidence: 99%

The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

Markiewicz

Zawieska

2019

Applied Sciences

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This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested.

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Section: Related Workmentioning

confidence: 99%

The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

Markiewicz

Zawieska

2019

Applied Sciences

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“…This is the shortcoming of the ICP algorithm. Overall, the ICP algorithm can achieve high accuracy in point cloud registration [42]. In this paper, the traditional ICP algorithm is used for experiments.…”

Section: Accuracymentioning

confidence: 99%

Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Wang

Gao

2019

Sensors

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Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

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“…To align overlapped point clouds captured at different positions, the coarse alignment and the fine alignment are involved: (a) point, linear or planar features are extracted in an automatic process; (b) a manual selection process is performed or an automatic matching progress utilizing the RANSAC strategy is implemented to match the primitives, solving the rotation matrix and the translation to provide a coarsely alignment, since the uneven distribution of features leaves residuals in the alignments; and (c) a fine registration process, which is usually based on Iterative Closest Point (ICP) algorithm, is conducted to achieve high-accuracy alignments [49][50][51][52][53]. However, the global ICP process would introduce new errors to the registration as the low-resolution and inhomogeneous distribution of points influences the iterative approximation results and generate misalignments.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

Fan¹,

Shi²,

Xiang³

et al. 2019

Remote Sensing

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Over the last decade, increasing demands for building interior mapping have brought the challenge of effectively and efficiently acquiring geometric information. Most mobile mapping methods rely on the integration of Simultaneous Localization And Mapping (SLAM) and costly Inertial Measurement Units (IMUs). Meanwhile, the methods also suffer misalignment errors caused by the low-resolution inhomogeneous point clouds captured using multi-line Mobile Laser Scanners (MLSs). While point-based alignments between such point clouds are affected by the highly dynamic moving patterns of the platform, plane-based methods are limited by the poor quality of the planes extracted, which reduce the methods’ robustness, reliability, and applicability. To alleviate these issues, we proposed and developed a method for plane extraction from low-resolution inhomogeneous point clouds. Based on the definition of virtual scanlines and the Enhanced Line Simplification (ELS) algorithm, the method extracts feature points, generates line segments, forms patches, and merges multi-direction fractions to form planes. The proposed method reduces the over-segmentation fractions caused by measurement noise and scanline curvature. A dedicated plane-to-plane point cloud alignment workflow based on the proposed plane extraction method was created to demonstrate the method’s application. The implementation of the coarse-to-fine procedure and the shortest-path initialization strategy eliminates the necessity of IMUs in mobile mapping. A mobile mapping prototype was designed to test the performance of the proposed methods. The results show that the proposed workflow and hardware system achieves centimeter-level accuracy, which suggests that it can be applied to mobile mapping and sensor fusion.

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Registration of Laser Scanning Point Clouds: A Review

Cited by 214 publications

References 162 publications

The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration

Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

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