Robust cylinder fitting in laser scanning point cloud data

Nurunnabi, Abdul; Sadahiro, Yukio; Lindenbergh, Roderik; Belton, David

doi:10.1016/j.measurement.2019.01.095

Cited by 57 publications

(40 citation statements)

References 43 publications

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“…Figure 3 shows the ellipse diagram. Ellipse can be described by five parameters [25][26][27][28][29] The general ellipse equation can be presented as, arctan…”

Section: Optimal Ellipse Center Extraction Methodsmentioning

confidence: 99%

“…Over the past years, many ellipse detection algorithms sprang up and were studied broadly to solve the ellipse detection problem, which can be briefly grouped into several categories: Hough transform method [21][22][23][24], leastsquare fitting method [25][26][27][28][29], clustering method [30][31][32][33][34]. Hough transform has been widely used for detecting geometric primitives such as line segment, circle and ellipse.…”

Section: Introductionmentioning

confidence: 99%

“…However, the above ellipse fitting methods [21][22][23][24][25][26][27][28][29][30][31][32][33][34] are sensitive to noise, which will reduce the accuracy of camera calibrat ion with circular feature target. In order to achieve higher accuracy of ellipse center extraction and reduce the calibrat ion error, we develop a high robust mu lti-ellipse center extraction (HRM CE) method based on the dynamic granularity matrix space(DGM S) model.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Xue

2020

IEEE Access

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Ellipse center extract ion is the important basis of camera calib ration with circle arrays calibrat ion board, wh ich direct ly affects the measurement accuracy of mach ine vision system. A iming at the problem of high noise sensitivity of the most ellipse center extract ion methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of mult i-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jump ing and transformat ion of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center ext raction method are proposed based on the dynamic granularity matrix space model. Experimental results sh ow that the proposed method has higher accuracy, robustness and lower camera calibration error. INDEX TERMS multi-ellipse center, dynamic granularity matrix space, sub-pixel edge segmentation, hierarchical structure, camera calibration.

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“…Figure 3 shows the ellipse diagram. Ellipse can be described by five parameters [25][26][27][28][29] The general ellipse equation can be presented as, arctan…”

Section: Optimal Ellipse Center Extraction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Xue

2020

IEEE Access

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“…According to [29], the cylinder primitive is the most robust primitive in terms of representing the geometry of tree branches, even with holes and noises in the dataset. Moreover, compared with the complex curve fitting method, cylinder fitting is relatively easy and fast in computation [30,31]. Figure 8 shows in general how cylinder fitting is employed to obtain a tree model with branches.…”

Section: Branch Fittingmentioning

confidence: 99%

AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees

et al. 2019

Self Cite

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Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. However, some works cannot guarantee sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct detailed 3D tree models from laser scans. We first extract an initial tree skeleton from the input point cloud by establishing a minimum spanning tree using the Dijkstra shortest-path algorithm. Then, the initial tree skeleton is pruned by iteratively removing redundant components. After that, an optimization-based approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The overall fitting error (i.e., the distance between the input points and the output model) is less than 10 cm. The reconstructed tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc. The source code of this work is freely available at https://github.com/tudelft3d/adtree.

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“…It was concluded that no single algorithm provides the best solution. Geometric primitive reconstruction [6,[15][16][17] is an important problem in the field of computer-aided design (CAD).…”

Section: Introductionmentioning

confidence: 99%

Reproducibility Experimentation among Computer-Aided Inspection Software from a Single Point Cloud

Jbira

Tahan

Bonsaint³

et al. 2019

Journal of Control Science and Engineering

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The ISO GPS and ASME Y14.5 standards have defined dimensional and geometrical tolerance as a way to express the limits of surface part variations with respect to nominal model surfaces. A quality-control process using a measuring device verifies the conformity of the parts to these tolerances. To convert the control measurement points as captured by a device such as a coordinate measurement machine (CMM) or noncontact scan, it is necessary to select the appropriate algorithm (e.g., least square size and maximum inscribed size) and to include the working hypotheses (e.g., treatment of outliers, noise filtering, and missing data). This means that the operator conducting the analysis must decide on which algorithm to use. Through a literature review of current software programs and algorithms, many inaccuracies were found. A benchmark was therefore developed to compare the algorithm performance of three computer-aided inspection (CAI) software programs. From the same point cloud and on the same specifications (requirements and tolerances), three CAI options have been tested with several dimensional and geometrical features.

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Robust cylinder fitting in laser scanning point cloud data

Cited by 57 publications

References 43 publications

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees

Reproducibility Experimentation among Computer-Aided Inspection Software from a Single Point Cloud

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