Point cloud uncertainty analysis for laser radar measurement system based on error ellipsoid model

Du, Zhimin; Wu, Zhaoyong; Yang, Jing

doi:10.1016/j.optlaseng.2015.11.010

Cited by 18 publications

(8 citation statements)

References 9 publications

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“…For each point, the variance propagation process provides a 3×3 covariance matrix, Σ , as measurement of spatial uncertainty. Closed formulations of the 3D positional uncertainty, Σ , as a function of instrumental measurement errors can be found in Maalek et al [11] for TLS ( Figure 1a), Zhengchun et al [28] for laser radar measurement system (LRMS), Mourikis and Roumeliotis [32] for SLAM-based instruments (Figure 1b), and Beder and Steffen [33] for SfMbased techniques (Figure 1c). For each point, , the corresponding spatial uncertainty, Σ , can be used to construct an error ellipsoid as follows:…”

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 92%

“…This shows that if, for instance, a fixed distance threshold of say 5mm is adopted to assign points around the mode of the histogram as floors, 99% of the TLS and only 32% of the smartphone 3D reconstruction points will be identified as belonging to the floor. Therefore, a successful distance threshold ( Figure 1f) must incorporate the expected point measurement uncertainties related to a particular point cloud instrument [11,[27][28][29]. Given a calibrated point cloud collection instrument (assumption of modeled systematic errors), the point measurement uncertainties in the Cartesian coordinate system (i.e.…”

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 99%

“…Given a calibrated point cloud collection instrument (assumption of modeled systematic errors), the point measurement uncertainties in the Cartesian coordinate system (i.e. spatial uncertainty [28]) can be modeled through the propagation of instrumental measurement errors, typically approximated to the first order [30]. For instance in TLS, the spatial uncertainty of points must incorporate the raw instrumental measurement errors (e.g.…”

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 99%

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Field Information Modeling (FIM)™: Best Practices using Point Clouds

Maalek¹

2021

Preprint

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This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

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Section: Point Cloud Spatial Uncertaintymentioning

confidence: 92%

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 99%

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 99%

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Field Information Modeling (FIM)™: Best Practices using Point Clouds

Maalek¹

2021

Preprint

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“…This shows that if, for instance, a fixed distance threshold of, say, 5 mm is adopted to assign points around the mode of the histogram as floors, 99% of the TLS and only 32% of the smartphone 3D reconstruction points will be identified as belonging to the floor. Therefore, a successful distance threshold ( Figure 1f) must incorporate the expected point measurement uncertainties related to a particular point cloud instrument [16,26,38,39].…”

Section: Point Cloud Spatial Uncertaintymentioning

confidence: 99%

Field Information Modeling (FIM)™: Best Practices Using Point Clouds

Maalek

2021

Remote Sensing

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This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets.

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“…The parameters e i , 1 , e i , 2 and e i , 3 are samples from a statistical distribution with expectation value zero and standard deviations e i, 1 , e i, 2 and e i, 3 , respectively.…”

Section: Laser Tracker Uncertainty Modelmentioning

confidence: 99%

Uncertainty evaluation and optimization of INS installation measurement using Monte Carlo Method

Wang

Huang

et al. 2015

Assembly Automation

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to increase the measurement accuracy of assembly deviations of an inertial navigation system, a new evaluation and optimal method of assembly metrology system is proposed, which takes into account the uncertainty from laser tracker hardware and coordinate system transformation, and is based on the Monte Carlo method. Design/methodology/approach -The uncertainty model of the laser tracker is established and its parameters are obtained from the known repeated test data by kriging interpolation and the least squares method. The errors of coordinate transformation are reduced by using a weighted point matching method, and the uncertainty of the transformation parameters is obtained based on the generalized inverse theory. The weighting coefficients of each reference point are optimized by the particle swarm optimization method according to the assembly requirements. Findings -The experiment results show that measurement error and predicted results match well, and the assembly deviation uncertainty of large component is reduced by about 10 per cent compared with the singular value decomposition method. Originality/value -This paper proposes a method to evaluate and eliminate the influence of random errors of the laser tracker during evaluation process of coordinate translation parameters and assembly deviations. The proposed method would be useful to improve the assembly measurement accuracy through less measurement times.

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Point cloud uncertainty analysis for laser radar measurement system based on error ellipsoid model

Cited by 18 publications

References 9 publications

Field Information Modeling (FIM)™: Best Practices using Point Clouds

Field Information Modeling (FIM)™: Best Practices using Point Clouds

Field Information Modeling (FIM)™: Best Practices Using Point Clouds

Uncertainty evaluation and optimization of INS installation measurement using Monte Carlo Method

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