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Advances in Localization and Navigation (GIS Ostrava 2021)
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Advances in Localization and Navigation (GIS Ostrava 2021)

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 16342

Special Issue Editors

Dr. Guenther Retscher

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Guest Editor
Engineering Geodesy, Institute of Geodesy and Geophysics, Vienna University of Technology, 1040 Vienna, Austria
Interests: positioning and navigation with GNSS; location-based services (LBS); indoor and pedestrian navigation; applications of multi-sensor systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ondrej Krejcar

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Guest Editor
Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 50003 Hradec Kralove, Czech Republic
Interests: control systems; smart sensors; ubiquitous computing; manufacturing; wireless technology; portable devices; biomedicine; image segmentation and recognition; biometrics; technical cybernetics
Special Issues, Collections and Topics in MDPI journals
Dr. Vassilis Gikas

E-Mail Website
Guest Editor
Unit of Spatial Planning and Regional Development – SPReD, School of Rural and Surveying Engineering, Natonionla Technical University of Athens NTUA., Zographou Campus, 15780 Zografou, Greece
Interests: sensor fusion and Kalman filtering for navigation, engineering surveying and structural deformation monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Michal Kačmařík

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Guest Editor
Department of Geoinformatics, VŠB – Technical University of Ostrava, 17. listopadu 2172/15, 70800 Ostrava, Czech Republic
Interests: satellite positioning and navigation; GNSS meteorology; location-based services; spatial data collection; remote sensing; unmanned air vehicles; natural hazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current globalized world counts on localized data and location-aware services in a broad list of domains. People use their mobile devices for navigation in day-to-day professional and leisure time activities, companies automate their production or distribution, (unmanned) vehicles autonomously carry out given tasks separately or even cooperatively, and practically everything can be tracked during its movement. Seamless solutions for outdoor and also indoor mapping and navigation are being driven, based on diverse technologies and their fusions. Experts in geoinformatics, electronics, and other fields meet in multidisciplinary teams to address new challenges.

This Special Issue, which stems from the conference “GIS Ostrava 2021: Advances in Localization and Navigation”, welcomes submissions on methodological or applied aspects focused on (but not limited to) the following topics:

  • Localization via wireless networks
  • Satellite positioning and navigation
  • Vision-based localization and navigation
  • Inertial localization and navigation
  • Fusion of sensors and technologies
  • Autonomous operation of vehicles and robots
  • Simultaneous navigation and mapping (SLAM)
  • Cooperative localization and navigation, intelligent transportation
  • Artificial intelligence in localization and navigation
  • Indoor navigation
  • Pedestrian navigation
  • Location-based services
  • High-definition maps
  • Standards and interoperability
  • Algorithms and software development

Dr. Guenther Retscher
Prof. Dr. Ondrej Krejcar
Prof. Dr. Vassilis Gikas
Dr. Michal Kačmařík
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. ISPRS International Journal of Geo-Information 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 1700 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

  • positioning
  • navigation
  • sensor fusion
  • satellite positioning
  • vision-based navigation
  • inertial navigation
  • autonomous operation
  • localization and simultaneous localization and mapping (SLAM)
  • indoor navigation
  • location-based services

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Published Papers (6 papers)

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Research

18 pages, 23574 KiB  
Article
Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor
by Hongji Cao, Yunjia Wang, Jingxue Bi, Meng Sun, Hongxia Qi and Shenglei Xu
ISPRS Int. J. Geo-Inf. 2021, 10(10), 706; https://doi.org/10.3390/ijgi10100706 - 15 Oct 2021
Cited by 2 | Viewed by 1683
Abstract
Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. [...] Read more.
Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. In the offline stage, the received signal strength (RSS) measurements of the 2.4 GHz and 5 GHz signals at the reference points (RPs) are collected and normalized to generate the online dual-band fingerprint, a special fingerprint for dual-band Wi-Fi. Then, a dual-band fingerprint database, which is a dedicated fingerprint database for dual-band Wi-Fi, is built with the dual-band fingerprint and the corresponding RP coordinates. Each dual-band fingerprint constructs its positioning model with the GPR algorithm based on itself and its neighborhood fingerprints, and its corresponding RP coordinates are the label of this model. The neighborhood fingerprints are found by the spatial distances between RPs. In the online stage, the measured RSS values of dual-band Wi-Fi are used to generate the online dual-band fingerprint and the 5 GHz fingerprint. Due to the better stability of the 5 GHz signal, an initial position is solved with the 5 GHz fingerprint and the KNN algorithm. Then, the distances between the initial position and model labels are calculated to find a positioning model with the minimum distance, which is the optimal positioning model. Finally, the dual-band fingerprint is input into this model, and the output of this model is the final estimated position. To evaluate the proposed method, we selected two scenarios (A and B) as the test area. In scenario A, the mean error (ME) and root-mean-square error (RMSE) of the proposed method were 1.067 and 1.331 m, respectively. The ME and RMSE in scenario B were 1.432 and 1.712 m, respectively. The experimental results show that the proposed method can achieve a better positioning effect compared with the KNN, Rank, Coverage-area, and GPR algorithms. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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31 pages, 8839 KiB  
Article
The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones
by Zun Niu, Fugui Guo, Qiangqiang Shuai, Guangchen Li and Bocheng Zhu
ISPRS Int. J. Geo-Inf. 2021, 10(10), 699; https://doi.org/10.3390/ijgi10100699 - 14 Oct 2021
Cited by 5 | Viewed by 3112
Abstract
The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK [...] Read more.
The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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18 pages, 864 KiB  
Article
A Geometric Layout Method for Synchronous Pseudolite Positioning Systems Based on a New Weighted HDOP
by Xinyang Zhao, Qiangqiang Shuai, Guangchen Li, Fangzhou Lu and Bocheng Zhu
ISPRS Int. J. Geo-Inf. 2021, 10(9), 601; https://doi.org/10.3390/ijgi10090601 - 12 Sep 2021
Cited by 3 | Viewed by 2168
Abstract
The positioning accuracy of a ground-based system in an indoor environment is closely related to the geometric configuration of pseudolites. This paper presents a simple closed-form equation for computing the weighted horizontal dilution of precision (WHDOP) with four eigenvalues, which can reduce the [...] Read more.
The positioning accuracy of a ground-based system in an indoor environment is closely related to the geometric configuration of pseudolites. This paper presents a simple closed-form equation for computing the weighted horizontal dilution of precision (WHDOP) with four eigenvalues, which can reduce the amount of calculation. By comparing the result of WHDOP with traditional matrix inversion operation, the effectiveness of WHDOP of the proposed simple calculation method is analyzed. The proposed WHDOP has a linear relationship with the actual static positioning result error in an indoor environment proved by the Pearson analysis method. Twenty positioning points are randomly selected, and the positioning variance and WHDOP of each positioning point have been calculated. The correlation coefficient of WHDOP and the positioning variance is calculated to be 0.82. A pseudolite system layout method based on a simulated annealing algorithm is proposed by using WHDOP, instead of Geometric dilution of precision (GDOP). In this paper, the constraints of time synchronization are discussed. In wireless connection system, the distance between master station and slave station should be kept within a certain range. Specifically, for a given indoor scene, many positioning target points are randomly generated in this area by using the Monte Carlo method. The mean WHDOP value of all positioning points corresponding to the synchronous pseudolite layout is used as the objective function. The results of brute force search are compared with the method, which proves the accuracy of the new algorithm. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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18 pages, 13601 KiB  
Article
Accuracy Evaluation of Ionospheric Delay from Multi-Scale Reference Networks and Its Augmentation to PPP during Low Solar Activity
by Lewen Zhao, Jan Douša and Pavel Václavovic
ISPRS Int. J. Geo-Inf. 2021, 10(8), 516; https://doi.org/10.3390/ijgi10080516 - 30 Jul 2021
Cited by 5 | Viewed by 2268
Abstract
The Precise Point Positioning (PPP) with fast integer ambiguity resolution (PPP-RTK) is feasible only if the solution is augmented with precise ionospheric parameters. The vertical ionospheric delays together with the receiver hardware biases, are estimated simultaneously based on the uncombined PPP model. The [...] Read more.
The Precise Point Positioning (PPP) with fast integer ambiguity resolution (PPP-RTK) is feasible only if the solution is augmented with precise ionospheric parameters. The vertical ionospheric delays together with the receiver hardware biases, are estimated simultaneously based on the uncombined PPP model. The performance of the ionospheric delays was evaluated and applied in the PPP-RTK demonstration during the low solar activity period. The processing was supported by precise products provided by Deutsches GeoForschungsZentrum Potsdam (GFZ) and also by real-time products provided by the National Centre for Space Studies (CNES). Since GFZ provides only precise orbits and clocks, other products needed for ambiguity resolution, such as phase biases, were estimated at the Geodetic Observatory Pecny (GOP). When ambiguity parameters were resolved as integer values in the GPS-only solution, the initial convergence period was reduced from 30 and 20 min to 24 and 13 min when using CNES and GFZ/GOP products, respectively. The accuracy of ionospheric delays derived from the ambiguity fixed PPP, and the CODE global ionosphere map were then assessed. Comparison of ambiguity fixed ionospheric delay obtained at two collocated stations indicated the accuracy of 0.15 TECU for different scenarios with more than 60% improvement compared to the ambiguity float PPP. However, a daily periodic variation can be observed from the multi-day short-baseline ionospheric residuals. The accuracy of the interpolated ionospheric delay from global maps revealed a dependency on the location of the stations, ranging from 1 to 3 TECU. Precise ionospheric delays derived from the EUREF permanent network with an inter-station distance larger than 73 km were selected for ionospheric modeling at the user location. Results indicated that the PPP ambiguity resolution could be achieved within three minutes. After enlarging the inter-station distance to 209 km, ambiguity resolution could also be achieved within several minutes. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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18 pages, 4294 KiB  
Article
INS Error Estimation Based on an ANFIS and Its Application in Complex and Covert Surroundings
by Yabo Duan, Huaizhan Li, Suqin Wu and Kefei Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(6), 388; https://doi.org/10.3390/ijgi10060388 - 4 Jun 2021
Cited by 9 | Viewed by 2392
Abstract
Inertial navigation is a crucial part of vehicle navigation systems in complex and covert surroundings. To address the low accuracy of vehicle inertial navigation in multifaced and covert surroundings, in this study, we proposed an inertial navigation error estimation based on an adaptive [...] Read more.
Inertial navigation is a crucial part of vehicle navigation systems in complex and covert surroundings. To address the low accuracy of vehicle inertial navigation in multifaced and covert surroundings, in this study, we proposed an inertial navigation error estimation based on an adaptive neuro fuzzy inference system (ANFIS) which can quickly and accurately output the position error of a vehicle end-to-end. The new system was tested using both single-sequence and multi-sequence data collected from a vehicle by the KITTI dataset. The results were compared with an inertial navigation system (INS) position solution method, artificial neural networks (ANNs) method, and a long short-term memory (LSTM) method. Test results indicated that the accumulative position errors in single sequence and multi-sequences experiments decreased from 9.83% and 4.14% to 0.45% and 0.61% by using ANFIS, respectively, which were significantly less than those of the other three approaches. This result suggests that the ANFIS can considerably improve the positioning accuracy of inertial navigation, which has significance for vehicle inertial navigation in complex and covert surroundings. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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17 pages, 1515 KiB  
Article
Indoor Floor Localization Based on Multi-Intelligent Sensors
by Min Zhao, Danyang Qin, Ruolin Guo and Xinxin Wang
ISPRS Int. J. Geo-Inf. 2021, 10(1), 6; https://doi.org/10.3390/ijgi10010006 - 25 Dec 2020
Cited by 11 | Viewed by 2550
Abstract
With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large [...] Read more.
With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL. Full article
(This article belongs to the Special Issue Advances in Localization and Navigation (GIS Ostrava 2021))
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