100 research outputs found
Exploiting Radio Fingerprints for Simultaneous Localization and Mapping
Simultaneous localization and mapping (SLAM) is paramount for unmanned
systems to achieve self-localization and navigation. It is challenging to
perform SLAM in large environments, due to sensor limitations, complexity of
the environment, and computational resources. We propose a novel approach for
localization and mapping of autonomous vehicles using radio fingerprints, for
example WiFi (Wireless Fidelity) or LTE (Long Term Evolution) radio features,
which are widely available in the existing infrastructure. In particular, we
present two solutions to exploit the radio fingerprints for SLAM. In the first
solution-namely Radio SLAM, the output is a radio fingerprint map generated
using SLAM technique. In the second solution-namely Radio+LiDAR SLAM, we use
radio fingerprint to assist conventional LiDAR-based SLAM to improve accuracy
and speed, while generating the occupancy map. We demonstrate the effectiveness
of our system in three different environments, namely outdoor, indoor building,
and semi-indoor environment.Comment: This paper has been accepted by IEEE Pervasive Computing with DOI:
10.1109/MPRV.2023.327477
Real-world deployment of low-cost indoor positioning systems for industrial applications
The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building's floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building.This work was supported in part by the Fundacao para a Ciencia e Tecnologia-FCT through the Research and Development Units Project Scope under Grant UIDB/00319/2020 and in part by the Ph.D. Fellowship under Grant PD/BD/137401/2018. The associate editor coordinating the review of this article and approving it for publication was Prof. Masanori Sugimoto
A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing
Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem
CrowdFusion: Multi-Signal Fusion SLAM Positioning Leveraging Visible Light
With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field and WiFi to achieve sub-meter accuracy. We propose a CrowdFusion Simultaneous Localization and Mapping (SLAM) comprised of coarse-grained and fine-grained trace merging respectively based on the Iterative Closest Point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67m for the merged traces and 0.77m for positioning, merely replying on crowdsourcing traces without professional calibration
Self-healing radio maps of wireless networks for indoor positioning
Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real
nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente,
a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais
possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos,
sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os
sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados
para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições
conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação,
adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente
em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam
em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que
seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento
para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas
de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de
movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas
duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a
localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar
quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração
nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências
sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços
de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which
benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring,
improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be
exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and
interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they
represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi-
Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation
effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated
to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming
and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly
present in industrial environments, can be explored to help build and maintain radio maps, as long as it
is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial
vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked
using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the
self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are
proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when
Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human
effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this
solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and
analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments
Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
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