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GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (18 January 2022) | Viewed by 19470

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Prof. Dr. Antonio Vettore

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Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, via dell’Università 16, 35020 Legnaro (PD), Italy
Interests: geomatics; mobile mapping; laser scanning; photogrammetry; remote sensing; navigation; unmanned aerial vehicles; cultural heritage; GIS
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Dr. Andrea Masiero

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Dipartimento di Ingegneria Civile e Ambientale (DICEA), University of Florence, 50121 Florence, Italy
Interests: geomatics; mobile mapping; positioning and navigation; machine learning to computer vision; smart camera networks; modeling and control of adaptive optics systems
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Prof. Dr. Naser El-Sheimy

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Department of Geomatics Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent and autonomous systems; navigation & positioning technologies; satellite technologies; multi-sensor systems; wireless positioning; vehicles & transportation systems; driverless cars; technology development; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent electronic improvements have allowed the development of a number of portable mapping systems, where the combination of a reliable navigation system with mapping sensors enables the quick acquisition of accurate geospatial data. Thanks to their flexibility, such systems are becoming popular in a number of applications, in particular related to environmental and infrastructure monitoring.

This Special Issue aims at fostering the spread of recent research results concerning technological, methodological, and geospatial data processing aspects and case studies related to the considered topics.

Topics include but are not limited to development of new positioning and navigation approaches, based on GPS/INS and other sensors, also in a simultaneous localization and mapping strategy; development and testing of mapping systems, based on the fusion of information provided by different sensors, such as regular and multispectral cameras, LiDAR, and RADAR; and analysis and processing of geospatial information for environment and infrastructure monitoring, also based on artificial intelligence and machine learning techniques.

Prof. Dr. Antonio Vettore
Prof. Dr. Andrea Masiero
Prof. Dr. Naser El-Sheimy
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. Remote Sensing 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 2700 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

GPS/INS
Sensor fusion
Environmental monitoring
Infrastructure monitoring
Photogrammetry
LiDAR
Multispectral–hyperspectral imaging
Mapping and data analysis
UAV

Published Papers (6 papers)

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Research

16 pages, 19270 KiB

Open AccessArticle

Rapid Inspection of Large Concrete Floor Flatness Using Wheeled Robot with Aided-INS

by Zhipeng Chen, Qingquan Li, Weixin Xue, Dejin Zhang, Siting Xiong, Yu Yin and Shiwang Lv

Remote Sens. 2022, 14(7), 1528; https://doi.org/10.3390/rs14071528 - 22 Mar 2022

Viewed by 2884

Abstract

Flatness is an important parameter for the quality assessment of concrete floors. Traditional flatness inspection methods have problems with sparse sampling and low efficiency for large concrete floors. In this paper, a rapid flatness inspection method for large concrete floors based on a wheeled robot with an aided inertial navigation system (INS) is proposed. The robot realizes high precision relative to the three-dimensional profile measurement of concrete floors through fusion of INS, odometers and total station. The overall measurement of concrete floor flatness is realized through a certain density of profiles. The measurement performance of the proposed method has been tested in laboratory, and the effectivity is tested in the flatness inspection of the concrete base of an ice floor in the National Speed Skating Oval of 2022 Beijing Winter Olympic Games. The results demonstrate that the floor flatness inspection accuracy can meet the requirement of ±0.5 mm over 5 m and the efficiency is several times that of the traditional method. This technology is promising for high precision and rapid flatness inspection of large floors. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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19 pages, 8305 KiB

Open AccessArticle

A Comparative Analysis of Unmanned Aircraft Systems in Low Altitude Photogrammetric Surveys

by Francesco Mugnai and Grazia Tucci

Remote Sens. 2022, 14(3), 726; https://doi.org/10.3390/rs14030726 - 03 Feb 2022

Cited by 4 | Viewed by 2693

Abstract

Comparing photogrammetric performances of four user-grade unmanned aircraft systems (UAS) is the main aim of this paper. This study investigates what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental and restoration purposes. Some photogrammetric surveys were conducted in a 5 ha area using a Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2 and Mavic Mini 2. These unmanned aircrafts are commercial systems used mainly by private professionals. Some photogrammetric reconstructions were carried out by varying flight altitude and camera settings of the 4 UAS. Structure-from-motion (SfM) algorithms were applied to the images taken from the UASs. The surveys’ quality was analyzed by comparing the ground targets’ coordinates measured on the field with indirect georeferencing through global navigation satellite system (GNSS). Fifty targets were installed and arranged following a kind of regular grid. For each photogrammetric flight, the boundary conditions were maintained the same, as well as the flight trajectories and the ground control point distribution. Altimetric and planimetric residuals were reported and compared for each photogrammetric survey. Using a regular grid of ground targets, the result obtained from Phantom 4 is one order of magnitude better than the ones obtained from the other UASs. Mavic Mini 2 leads to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (it does not require a pilot’s license), it could significantly reduce costs compared to all the others. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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35 pages, 12037 KiB

Open AccessArticle

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

by Andrea Masiero, Charles Toth, Jelena Gabela, Guenther Retscher, Allison Kealy, Harris Perakis, Vassilis Gikas and Dorota Grejner-Brzezinska

Remote Sens. 2021, 13(23), 4858; https://doi.org/10.3390/rs13234858 - 30 Nov 2021

Cited by 24 | Viewed by 2736

Abstract

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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18 pages, 5826 KiB

Open AccessArticle

Spherically Optimized RANSAC Aided by an IMU for Fisheye Image Matching

by Anbang Liang, Qingquan Li, Zhipeng Chen, Dejin Zhang, Jiasong Zhu, Jianwei Yu and Xu Fang

Remote Sens. 2021, 13(10), 2017; https://doi.org/10.3390/rs13102017 - 20 May 2021

Cited by 7 | Viewed by 2622

Abstract

Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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20 pages, 8883 KiB

Open AccessArticle

A Real-Time GNSS/PDR Navigation System for Mobile Devices

by Michele Basso, Alessio Martinelli, Simone Morosi and Fabrizio Sera

Remote Sens. 2021, 13(8), 1567; https://doi.org/10.3390/rs13081567 - 18 Apr 2021

Cited by 11 | Viewed by 3226

Abstract

In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user’s motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worn location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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29 pages, 5880 KiB

Open AccessArticle

Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

by Kamil Maciuk, Michal Apollo, Joseph M. Cheer, Ondřej Konečný, Krystian Kozioł, Jacek Kudrys, Joanna Mostowska, Marta Róg, Bogdan Skorupa and Stanisław Szombara

Remote Sens. 2021, 13(6), 1111; https://doi.org/10.3390/rs13061111 - 15 Mar 2021

Cited by 2 | Viewed by 3865

Abstract

Mountain peaks and their altitude have been of interest to researchers across disciplines. Measurement methods and techniques have changed and developed over the years, leading to more accurate measurements and, consequently, more accurate determination of peak altitudes. This research transpired due to the frequency of misstatements found in existing sources including books, maps, guidebooks and the Internet. Such inaccuracies have the potential to create controversy, especially among peak-baggers in pursuit of climbing the highest summits. The Polish Sudetes Mountains were selected for this study; 24 summits in the 14 mesoregions were measured. Measurements were obtained employing the global navigation satellite system (GNSS) and light detection and ranging (LiDAR), both modern and highly precise techniques. Moreover, to determine the accuracy of measurements, several of the summits were measured using a mobile phone as an additional method. We compare GNSS vs. LiDAR and verify the level of confidence of peak heights obtained by automatic methods from LiDAR data alone. The GNSS receiver results showed a discrepancy of approximately 10 m compared with other information sources examined. Findings indicate that the heights of peaks presented in cartographic materials are inaccurate, especially in lesser-known mountain ranges. Furthermore, among all the mountain ranges examined, the results demonstrated that five of the summits were no longer classed as the highest, potentially impacting tourist perceptions and subsequent visitation. Overall, due to the topographical relief characteristics and varying vegetation cover of mountains, we argue that the re-measuring procedure should comprise two steps: (1) develop high-resolution digital elevation models (DEMs) based on LiDAR; (2) assumed heights should be measured using precise GNSS receivers. Unfortunately, due to the time constraints and the prohibitive costs of GNSS, LiDAR continues to be the most common source of new altitude data. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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