Showing papers by "Vassilis Gikas published in 2020"

Recent Advancement on the Use of Global Navigation Satellite System-Based Positioning for Intelligent Transport Systems [Guest Editorial]

Abstract: The articles in this special section focus on recent advancements on the use of the global navigation satellite system (GNSS)-based positioning for intelligent transport systems. The civil applications of geopositioning are undergoing exponential development. The latest market analysis for global navigation satellite systems (GNSSs) shows two major fields of application which share the majority of the market: intelligent transport systems (ITS), mainly in the road ITS domain, and location-based services, accessible on smartphones and tablets. The modernization of GPS and Russia’s GLONASS system and the development of Galileo and Bei- Dou are proceeding at a fast pace, introducing improved potential capabilities and higher performance levels for satellite-based positioning, and leading to new architectures for positioning and new strategies for positioning by means of other sensors. GNSSs are considered the superior system to provide accurate and global position, velocity, and time.

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

Abstract: Abstract Localization 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.

Indoor navigation and mapping: performance analysis of uwb-based platform positioning

Abstract: . The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.

A Benchmarking Measurement Campaign to Support Ubiquitous Localization in GNSS Denied and Indoor Environments

Abstract: Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. As part of the joint IAG/FIG Working Groups 4.1.1 and 5.5 on Multi-sensor Systems, a benchmarking measurement campaign was conducted at The Ohio State University. Initial experiments have demonstrated that Cooperative Localization (CL) is extremely useful for positioning and navigation of platforms navigating in swarms or networks. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were equipped with combinations of GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), Raspberry Pi units, cameras, Light Detection and Ranging (LiDAR) and inertial sensors for CL. Pedestrians wore a specially designed helmet equipped with some of these sensors. An overview of the experimental configurations, test scenarios, characteristics and sensor specifications is given. It has been demonstrated that all involved sensor platforms in the different test scenarios have gained a significant increase in positioning accuracy by using ubiquitous user localization. For example, in the indoor environment, success rates of approximately 97% were obtained using Wi-Fi fingerprinting for correctly detecting the room-level location of the user. Using UWB, decimeter-level positioning accuracy is demonstrable achievable under certain conditions. The full sets of data is being made available to the wider research community through the WG on request.