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Recent Advances in Automated Measuring Systems
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Recent Advances in Automated Measuring Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 12538

Special Issue Editors

Dr. Alonso Sanchez

E-Mail Website
Guest Editor
University Centre of Mérida, University of Extremadura, 06800 Mérida, Spain
Interests: sensors and devices; automated measuring systems; metrology; geometric auscultation; close-range photogrammetry techniques
Dr. Pedro Ortiz Coder

E-Mail Website
Guest Editor
University Centre of Mérida, University of Extremadura, 06800 Mérida, Spain
Interests: videogrammetry; automatic photogrammetry; handheld 3D scanners; mobile mapping systems; image-based 3D modelling

Special Issue Information

Dear Colleagues,

In the last decade, the automatic mapping of complex scenarios has become an objective in the context of mobile mapping systems (MMS). For this reason, the companies and research studies have been forced to develop systems integrating different sensors (like digital cameras, LiDAR, GNSS-INS systems, optical encoders, etc.), managed using complex algorithms, to automatically detect and survey the geometry and texture of the measured elements, providing fast and complete data, with the least and easiest possible intervention by the user. As a result, we can find a wide range of instruments, with different formats, mainly with the configurations of vehicle-based surveys, backpacks (or trollies) MMS, or the most simple, handheld instruments, which survey middle/big scenarios accurately and automatically.

This Special Issue aims to provide a forum about recent advancements in automated measuring systems for mobile mapping solutions, with high/middle definition and accurate detail, including both the description and validation of new data capture devices, the development of new algorithms for the management and exploitation of the information obtained, the improvement of standard working procedures, as well as the integration of the results in civil, architectural and archaeological applications.

Dr. Alonso Sanchez
Dr. Pedro Ortiz Coder
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • Multi-sensor integration and data fusion
  • Positioning and navigation techniques used in MMSs
  • Point cloud quality assessment and validation
  • Validation of MMSs in civil, architectural and archaeological applications
  • Integration of the information in BIM (building information modeling)

Published Papers (2 papers)

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Research

21 pages, 11737 KiB  
Article
Measurement and Estimation of Spectral Sensitivity Functions for Mobile Phone Cameras
by Shoji Tominaga, Shogo Nishi and Ryo Ohtera
Sensors 2021, 21(15), 4985; https://doi.org/10.3390/s21154985 - 22 Jul 2021
Cited by 32 | Viewed by 7282
Abstract
Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions [...] Read more.
Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the spectral sensitivities are estimated from the input data of color samples and the corresponding output RGB values from the camera. We first present an imaging system for direct measurements. A variety of mobile phone cameras are measured using the system to create a database of spectral sensitivity functions. The features of the measured spectral sensitivity functions are then studied using principal component analysis (PCA) and the statistical features of the spectral functions extracted. We next describe a normal method to estimate the spectral sensitivity functions using color samples and point out some drawbacks of the method. A method to solve the estimation problem using the spectral features of the sensitivity functions in addition to the color samples is then proposed. The estimation is stable even when only a small number of spectral features are selected. Finally, the results of the experiments to confirm the feasibility of the proposed method are presented. We establish that our method is excellent in terms of both the data volume of color samples required and the estimation accuracy of the spectral sensitivity functions. Full article
(This article belongs to the Special Issue Recent Advances in Automated Measuring Systems)
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20 pages, 5464 KiB  
Article
A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
by Sen Wang, Xiaohe Chen, Guanyu Ding, Yongyao Li, Wenchang Xu, Qinglei Zhao, Yan Gong and Qi Song
Sensors 2021, 21(13), 4479; https://doi.org/10.3390/s21134479 - 30 Jun 2021
Cited by 13 | Viewed by 4319
Abstract
This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of [...] Read more.
This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM. Full article
(This article belongs to the Special Issue Recent Advances in Automated Measuring Systems)
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