Comparison of Point Cloud Registration Algorithms for Better Result Assessment – Towards an Open-Source Solution

Lachat, Elise; Landes, Tania; Grussenmeyer, Pierre

doi:10.5194/isprs-archives-xlii-2-551-2018

Cited by 15 publications

(17 citation statements)

References 12 publications

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“…In the case of Samaipata rock, the main obstacle and difference between similar studies of deformation measurement [9], [10], [2] based on repeated TLS is the considerable disparity in the parameters of the two different instruments used for the scanning -the ILRIS-3D laser scanner produced by Optech in 2006 and the ScanStation P40 introduced by Leica Geosystems in 2016. Both scanners belong to two entirely different epochs of TLS technology development; therefore, their technical characteristics differ considerably.…”

Section: Określanie Stopnia Erozji I Tempa Rozprzestrzeniania Się Pormentioning

confidence: 99%

“…Proper preservation requires reliable tools that offer accurate diagnostics of current conditions of the site. The latest advances in surveying technology, particularly that of 3D terrestrial laser scanning (TLS), give the opportunity to study damage and material decay using analysis of 3D point clouds generated by different instruments and techniques [2], [3]. Among them, TLS data are widely and successfully used for structural health monitoring in both civil engineering [4] and cultural heritage [5].…”

Section: Introductionmentioning

confidence: 99%

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Determining the rate of erosion and lichen spread on Samaipata rock by comparing 3D laser scan results from two different surveying epochs

Kubicka¹,

Kościuk²

2020

Architectus

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The paper describes the possibility of using 3D laser scans from two different surveying epochs for structural health monitoring. It uses the results of two particular projects-the 2006 3D laser scanning of Samiapata rock by the University of Arkansas and the 2016 3D laser scanning by the Laboratory of 3D Laser Scanning at Wrocław University of Science and Technology-and discusses the methods, results, and limitations of comparing them.

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Section: Określanie Stopnia Erozji I Tempa Rozprzestrzeniania Się Pormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determining the rate of erosion and lichen spread on Samaipata rock by comparing 3D laser scan results from two different surveying epochs

Kubicka¹,

Kościuk²

2020

Architectus

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“…However, the accidental errors, which can be as large as 15.58 cm is also spotted, meaning there are still room for improvements in the future. Table 3 shows the detailed numerical statistics of the comparison [88][89][90][91][92]. The accuracy of the recovered trajectory is not considered as the assessment criterion, since the point distribution versus the TLS point cloud, the noise level of the point cloud produced, and the accuracy of the targets is capable of reflecting the accuracy level of the system, which is also the main requirement of applying such mobile mapping system.…”

Section: Plane-to-plane Alignments For Imu-free Mobile Mappingmentioning

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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“…Using as comparison parameter the scan rate of Time of Flight (ToF) devices produced for example by Trimble, the point per second rate has improved exponentially from 5000 points/second (Trimble GX) in 2005 [6] to 25,000 points/second (Trimble SX10) in 2017 [7] and 100,000 points/second (Trimble X7) in 2020 (https://geospatialx7.trimble.com, retrieved 22 January 2020). This is also supported by significant improvements in the software part, with workflow automation taking more and more importance aided by ever more powerful computing cores available for the user [8,9].…”

Section: Introductionmentioning

confidence: 99%

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Murtiyoso

Grussenmeyer

2020

Sensors

Self Cite

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3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.

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Comparison of Point Cloud Registration Algorithms for Better Result Assessment – Towards an Open-Source Solution

Cited by 15 publications

References 12 publications

Determining the rate of erosion and lichen spread on Samaipata rock by comparing 3D laser scan results from two different surveying epochs

Determining the rate of erosion and lichen spread on Samaipata rock by comparing 3D laser scan results from two different surveying epochs

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

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

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