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MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS
This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications
ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences
In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians'
accurate locations is crucial to traffic safety. Current systems adopt cameras
and wireless sensors to detect and estimate people's locations via sensor
fusion. Standard fusion algorithms, however, become inapplicable when
multi-modal data is not associated. For example, pedestrians are out of the
camera field of view, or data from camera modality is missing. To address this
challenge and produce more accurate location estimations for pedestrians, we
propose a Generative Adversarial Network (GAN) architecture. During training,
it learns the underlying linkage between pedestrians' camera-phone data
correspondences. During inference, it generates refined position estimations
based only on pedestrians' phone data that consists of GPS, IMU and FTM.
Results show that our GAN produces 3D coordinates at 1 to 2 meter localization
error across 5 different outdoor scenes. We further show that the proposed
model supports self-learning. The generated coordinates can be associated with
pedestrian's bounding box coordinates to obtain additional camera-phone data
correspondences. This allows automatic data collection during inference. After
fine-tuning on the expanded dataset, localization accuracy is improved by up to
26%
The four key challenges of advanced multisensor navigation and positioning
The next generation of navigation and positioning
systems must provide greater accuracy and reliability in a range
of challenging environments to meet the needs of a variety of
mission-critical applications. No single navigation technology is
robust enough to meet these requirements on its own, so a
multisensor solution is required. Although many new navigation
and positioning methods have been developed in recent years,
little has been done to bring them together into a robust, reliable,
and cost-effective integrated system. To achieve this, four key
challenges must be met: complexity, context, ambiguity, and
environmental data handling. This paper addresses each of these
challenges. It describes the problems, discusses possible
approaches, and proposes a program of research and
standardization activities to solve them. The discussion is
illustrated with results from research into urban GNSS
positioning, GNSS shadow matching, environmental feature
matching, and context detection
The IPIN 2019 Indoor Localisation CompetitionâDescription and Results
IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks
The IPIN 2019 Indoor Localisation Competition - Description and Results
IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks
Advanced Pedestrian Positioning System to Smartphones and Smartwatches
In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2
Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound
The average life expectancy has been increasing in the last decades, creating the need for
new technologies to improve the quality of life of the elderly. In the Ambient Assisted
Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their
autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require
expensive deployment and maintenance. On the other hand, most infrastructure-free
systems rely on a single source of information, being highly dependent on its availability.
Such systems will hardly be deployed in real-life scenarios, as they cannot handle the
absence of their source of information. An efficient solution must, thus, guarantee the
continuous indoor positioning of the elderly.
This work proposes a new room-level low-cost indoor location algorithm. It relies
on three information sources: inertial sensors, to reconstruct usersâ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi,
to estimate the distance to the Access Point in the neighbourhood. Two data collection
protocols were designed to resemble a real living scenario, and a data processing stage
was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions.
As each source provides different information to the classification, the data were merged
to produce a more robust localization. Three data fusion approaches (input-level, early,
and late fusion) were implemented for this goal, providing a final output containing
complementary contributions from all data sources.
Experimental results show that the performance improved when more than one source
was used, attaining a weighted F1-score of 81.8% in the localization between seven home
divisions. In conclusion, the evaluation of the developed algorithm shows that it can
achieve accurate room-level indoor localization, being, thus, suitable to be applied in
Ambient Assisted Living scenarios.O aumento da esperança mĂ©dia de vida nas Ășltimas dĂ©cadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos.
No Ăąmbito da AssistĂȘncia Ă Autonomia no DomicĂlio, sistemas de localização indoor tĂȘm
emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As
tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas
que necessitam de infrastruturas adicionais e aquelas que nĂŁo. Sistemas dependentes de
infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluçÔes que não requerem infrastrutura, dependem
de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que
não consegue lidar com a falta de informação de um sensor dificilmente serå implementado em cenårios reais. Uma solução eficiente deverå assim garantir o acompanhamento
contĂnuo dos idosos.
A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais,
capazes de reconstruir a trajetĂłria do utilizador; som, explorando as caracterĂsticas dis tintas de cada divisĂŁo da casa; e Wi-Fi, responsĂĄvel pela estimativa da distĂąncia entre o
ponto de acesso e o smartphone. Cada fonte sensorial, extraĂda dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada atravĂ©s de
algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas
fontes contĂȘm informação diferente acerca das mesmas caracterĂsticas do sistema, a sua
fusĂŁo torna a classificação mais informada e robusta. Com este objetivo, foram implementadas trĂȘs abordagens de fusĂŁo de dados (input data, early and late fusion), fornecendo um
resultado final derivado de contribuiçÔes complementares de todas as fontes de dados.
Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido
melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1-
score de 81.8% na distinção entre sete divisÔes domésticas. Concluindo, o algoritmo de
localização indoor, combinando informaçÔes de trĂȘs fontes diferentes atravĂ©s de mĂ©todos
de fusão de dados, alcançou uma localização room-level e estå apto para ser aplicado num
cenĂĄrio de AssistĂȘncia Ă Autonomia no DomicĂlio
Toward a unified PNT, Part 2: Ambiguity and environmental data: Two further key challenges of multisensor positioning
The coming requirements of greater accuracy and reliability in a range of challenging environments for a multitude of missioncritical applications require a multisensor approach and an over-arching methodology that does not yet exist. The likelihood depends on both the positioning method and the context, both environmental and behavioral. Urban and indoor positioning techniques that do not require dedicated infrastructure are particularly vulnerable to ambiguity. Even where a signal of opportunity is identifiable, the transmission site may change without warning. For example, Wi-Fi access points are sometimes moved and mobile phone networks are periodically refigured. Thus, there is a risk of false landmark identification. The pattern-matching positioning method maintains a database of measurable parameters that vary with position. Examples include terrain height, magnetic field variations, Wi-Fi signal strengths, and GNSS signal availability information
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