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Machines
Special Issues
Online/Onsite Optical Metrology Techniques: Challenges, Trends and Solutions
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Online/Onsite Optical Metrology Techniques: Challenges, Trends and Solutions

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 4635

Special Issue Editors

Dr. Xiaobo Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: optical metrology
Dr. Xiao Yang

E-Mail Website
Guest Editor
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: high precision vision measurement; digital holographic measurement technology; digital image correlation
Dr. Jinkai Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Jinan, Jinan 250022, China
Interests: optical metrology
Prof. Dr. Jinsong Bao

E-Mail Website
Guest Editor
College of Mechanical Engineering, University of Donghua, Shanghai 201620,China
Interests: Industry 4.0; intelligent manufacturing system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, online and onsite optical metrology techniques have received extensive attention for many dimensional control research and industrial applications. Some representative techniques adapt 2D imaging measurement, stereo vision, 3D laser scanning and structured light to acquire the product dimensional information to evaluate the manufacturing error or improve the sequential manufacturing process. Compared with the traditional manual or offline sampling inspection methods, these online and onsite optical metrology techniques have remarkable advantages in regard to accuracy, efficiency and the amount of collected data, thus changing the way the inspection applications are performed and providing new possibilities for online/onsite product quality control.

This Special Issue is focused on the study, research and discovery of the challenges, trends and solutions related to online and onsite optical metrology techniques. It includes the novel design of optical sensors, improved methods for measurement performance enhancement, novel mathematical methods for advanced data processing and various specific solutions for online/onsite optical metrology applications.

The technical scope of this Special Issue includes, but is not limited to:

  • 2D and 3D optical metrology techniques;
  • Accuracy and efficiency improvements for optical metrology;
  • Image and point cloud processing algorithms;
  • High reflective surface measurement;
  • Novel vision sensors and instruments;
  • Robot integrated optical metrology systems;
  • AI-assisted optical metrology;
  • Online/onsite optical metrology solutions;
  • Vision Detection or 3D Reconstruction using UAV;
  • High-precision vision measurement in vibration environment;
  • Digital image correlation technology under extreme conditions.

Dr. Xiaobo Chen
Dr. Xiao Yang
Dr. Jinkai Zhang
Prof. Dr. Jinsong Bao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical metrology
  • online/onsite inspection
  • data processing
  • error compensation
  • system modeling
  • system calibration
  • deep learning

Published Papers (3 papers)

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Research

19 pages, 10161 KiB  
Article
Advancing Simultaneous Localization and Mapping with Multi-Sensor Fusion and Point Cloud De-Distortion
by Haiyan Shao, Qingshuai Zhao, Hongtang Chen, Weixin Yang, Bin Chen, Zhiquan Feng, Jinkai Zhang and Hao Teng
Machines 2023, 11(6), 588; https://doi.org/10.3390/machines11060588 - 25 May 2023
Viewed by 1434
Abstract
This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces [...] Read more.
This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation. Full article
(This article belongs to the Special Issue Online/Onsite Optical Metrology Techniques: Challenges, Trends and Solutions)
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21 pages, 6207 KiB  
Article
A Self-Adaptive Multiple Exposure Image Fusion Method for Highly Reflective Surface Measurements
by Xiaobo Chen, Hui Du, Jinkai Zhang, Xiao Yang and Juntong Xi
Machines 2022, 10(11), 1004; https://doi.org/10.3390/machines10111004 - 31 Oct 2022
Cited by 1 | Viewed by 1248
Abstract
Fringe projection profilometry (FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring objects with highly reflective surfaces or high dynamic range surfaces remains challenging when using FPP. A number of multiple [...] Read more.
Fringe projection profilometry (FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring objects with highly reflective surfaces or high dynamic range surfaces remains challenging when using FPP. A number of multiple exposure image fusion methods have been proposed and successfully improved measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by practical experiences or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness with regard to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two areas of improvement relating to adaptively optimizing the initial exposure and the exposure sequence. First, by introducing the theory of information entropy, combined with an analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the basis of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are provided. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method via the measurement of objects with differences in surface reflectivity under different ambient lighting conditions. Full article
(This article belongs to the Special Issue Online/Onsite Optical Metrology Techniques: Challenges, Trends and Solutions)
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11 pages, 3301 KiB  
Article
Large-Scale Measurement Layout Optimization Method Based on Laser Multilateration
by Ying Sheng, Yukun Wang, Siwei Liu, Cuiping Wang and Juntong Xi
Machines 2022, 10(11), 988; https://doi.org/10.3390/machines10110988 - 28 Oct 2022
Viewed by 1235
Abstract
Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the [...] Read more.
Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the overall measurement accuracy, firstly, a measurement uncertainty model based on laser multilateration is established. Secondly, a fast laser intersection detection constraint algorithm based on a k-DOPS bounding box and an adaptive target ball incident angle constraint detection algorithm are established for large-scale measurement scenes. Finally, the constrained layout optimization of the laser trackers is realized by using an improved cellular genetic algorithm. The results show that the optimized system layout can achieve the full coverage of measurement points and has higher measurement accuracy. Compared with the traditional genetic algorithm, the improved cellular genetic algorithm converges faster and obtains a better position layout. Full article
(This article belongs to the Special Issue Online/Onsite Optical Metrology Techniques: Challenges, Trends and Solutions)
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