A benchmarking measurement campaign in GNSS-denied/challenged indoor/outdoor and transitional environments

Author:

Retscher Guenther1,Kealy Allison2,Gabela Jelena3,Li Yan4,Goel Salil5,Toth Charles K.6,Masiero Andrea7,Błaszczak-Bąk Wioleta8,Gikas Vassilis9,Perakis Harris9,Koppanyi Zoltan10,Grejner-Brzezinska Dorota6

Affiliation:

1. Vienna University of Technology, Vienna, Austria

2. 5376RMIT University, Melbourne, Australia

3. 2281The University of Melbourne, Melbourne, Australia

4. SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW2522, Australia

5. 30077Indian Institute of Technology Kanpur, Kanpur, India

6. The Ohio State University, Columbus, OH, USA

7. 87973University of Padova Faculty of Engineering, Padova, Italy

8. 49674University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

9. 416690National Technical University Athens, Athens, Greece

10. 166224Leica Geosystems AG, Heerbrugg, Switzerland

Abstract

AbstractLocalization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

Publisher

Walter de Gruyter GmbH

Subject

Earth and Planetary Sciences (miscellaneous),Engineering (miscellaneous),Modelling and Simulation

Reference60 articles.

1. High-dimensional Probabilistic Fingerprinting in Wireless Sensor Networks based on a Multivariate Gaussian Mixture Model;Sensors,2018

2. Positioning Slow Moving Platforms by UWB technology in GPS-Challenged Areas;ASCE Journal of Surveying Engineering,2017

3. Hallway based automatic indoor floorplan construction using room fingerprints

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