High-Precision Detection Method for Structure Parameters of Catenary Cantilever Devices using 3D Point Cloud Data

Wenqiang Liu, Zhigang Liu, Qiao Li, Zhiwei Han, Alfredo Nunez

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

This article proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCDs) using 3-D point cloud data. The steps of the proposed detection method are: 1) segmenting and recognizing the components of the catenary cantilever devices, 2) extracting the detection plane and backbone component axis of catenary cantilever devices, and 3) detecting the SPCCD. The effective segmentation of components is critical for structure parameter detection. A point cloud segmentation and recognition method based on three-dimensional convolutional neural networks (3-D CNNs) is introduced to determine the different components of the catenary cantilever devices. Compared with traditional unsupervised clustering procedures for point cloud segmentation, the proposed method can improve the segmentation accuracy, does not require complex tuning procedures of parameters, and improves robustness and stability. Additionally, the segmentation method defines a recognition function, facilitating the analysis of the structural relationship between objects. Furthermore, we proposed an improved projection random sample consensus (RANSAC) method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem. With RANSAC, it is also possible to precisely extract the backbone component axis and enhance parameter detection accuracy. The experimental results show that the structure angle and steady arm slope's error accuracy can achieve 0.1029° and 1.19%, respectively, which indicates the proposed approach can precisely detect the SPCCD.

Original languageEnglish
Article number3507811
Number of pages11
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
DOIs
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • 3D CNNs
  • Catenary cantilever devices
  • point cloud segmentation
  • RANSAC
  • structure parameter detection
  • three-dimensional convolutional neural networks (3-D CNNs)
  • random sample consensus (RANSAC)

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