Dynamic indoor localization using maximum likelihood particle filtering

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.Fil: Wang, Wenxu. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; Chin

3D indoor modeling and game theory based navigation for pre and post COVID-19 situation

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural facilities. There aren’t any maps available for most installations, and there are no proven techniques to categorize features within indoor areas to provide location-based services. During a pandemic like COVID-19, the direct connection between the masses is one of the significant preventive steps. Hospitals are the main stakeholders in managing such situations. This study presents a novel method to create an adaptive 3D model of an indoor space to be used for localization and routing purposes. The proposed method infuses LiDAR-based data-driven methodology with a Quantum Geographic Information System (QGIS) model-driven process using game theory. The game theory determines the object localization and optimal path for COVID-19 patients in a real-time scenario using Nash equilibrium. Using the proposed method, comprehensive simulations and model experiments were done using QGIS to identify an optimized route. Dijkstra algorithm is used to determine the path assessment score after obtaining several path plans using dynamic programming. Additionally, Game theory generates path ordering based on the custom scenarios and user preference in the input path. In comparison to other approaches, the suggested way can minimize time and avoid congestion. It is demonstrated that the suggested technique satisfies the actual technical requirements in real-time. As we look forward to the post-COVID era, the tactics and insights gained during the pandemic hold significant value. The techniques used to improve indoor navigation and reduce interpersonal contact within healthcare facilities can be applied to maintain a continued emphasis on safety, hygiene, and effective space management in the long term. The use of three-dimensional (3D) modeling and optimization methodologies in the long-term planning and design of indoor spaces promotes resilience and flexibility, encouraging the adoption of sustainable and safe practices that extend beyond the current pandemic

Desenvolvimento de uma aplicação móvel para traçado de percurso em ambiente indoor

Tese de mestrado, Sistemas de Informação Geográfica – Tecnologias e Aplicações, Universidade de Lisboa, Faculdade de Ciências, 2019Com as inovações tecnológicas da última metade de século, a tecnologia tem possibilitado ao ser humano alcançar sítios que antes eram inimagináveis. A área de posicionamento não é exceção à regra. Com a constante navegação no outdoor através de tecnologias como o GPS, apenas o indoor está inexplorado. Devido a limitações da tecnologia GPS tem-se observado um crescente interesse nesta temática com o uso de tecnologias alternativas para o posicionamento e navegação indoor. Neste documento será apresentado uma solução de posicionamento indoor somente com a utilização do Wi-Fi, sendo possível solicitar percursos que permitam a um utilizador chegar ao seu ponto de destino. Para tal será utilizado uma técnica de votação através de fingerprinting de uma rede previamente calibrada para encontrar a posição do utilizador. A área de interesse é o piso 1, do edifício C8 da Faculdade de Ciências da Universidade de Lisboa, e será realizado um trabalho que permita a um utilizador solicitar caminhos para pontos de interesse. Para tal, este trabalho tem como objetivo criar um Location Based Service, através da disponibilização de interfaces REST, que permita a sua reutilização noutros espaços de interesse sendo para tal apenas necessário o aprovisionamento correto dos dados. Os resultados consistiram no desenvolvimento de uma aplicação em Android que comunica com uma API de serviços disponibilizados num servidor aplicacional, permitindo assim que o utilizador desta aplicação possa solicitar caminhos para chegar a um ponto de interesse, sendo que, à medida que se desloca neste caminho a sua posição é atualizada com base no Wi-Fi.With the recent technological advances of the last half of a century, technology has been the enabler for the human being to reach unthinkable territories. The field of positioning is no exception. With the constant outdoor navigation through the use of technologies such as GPS, only the indoor is uncharted. Due to limitation of the GPS there has been an increase in the interest of these particular field with the use of complementary technologies for indoor positioning and navigation. This document will present a solution for indoor positioning strictly using Wi-Fi, allowing for an user to reach its destination point. For that it will be used a voting technique using a fingerprinting pre-calibrated network to find the position of the user. The area of interest will be the floor 1 on building C8 on the premises of faculty of sciences of the University of Lisbon that allows for a user to inquiry paths for a corresponding point of interest. For that, this project has the purposes of developing an agnostic LBS, through the exposure ofREST interfaces that allow its reusage in another spaces of interest in the premises, as long as the data is provided in a compliant way. The results were achieved through the development on an Android application that communicates with a pre-established API installed in an applicational server, allowing that an user of the application might query paths to reach its point of interest, being that, while he transverses this path its position is updated only with the resources of the Wi-Fi

Pattern detection platform using disruptive technologies to improve people’s daily tasks

Tesis por compendio de publicaciones[ES] En los últimos años la miniaturización de los dispositivos electrónicos y el abaratamiento de los procesos de fabricación de los componentes ha permitido que las redes de sensores inalámbricas sean cada vez mas importantes y se empleen en multitud de casos. Adicionalmente, y debido en parte a la mejora en cuanto a las capacidades de almacenamiento y procesamiento de datos se refiere, ha permitido construir sistemas sensibles al contexto en áreas como la medicina, la monitorización o la robótica que permiten hacer un análisis detallado y adaptable de los procesos y servicios que se pueden proporcionar a los usuarios. Esta tesis doctoral ha sido conformada mediante un “Compendio de Artículos” donde se analiza la aplicación de paradigmas de inteligencia artificial en 3 casos de estudio claramente diferenciados. Se ha planteado un novedoso sistema de localización en interiores que hace uso de técnicas bayesianas y fingerprinting, con objeto de automatizar y facilitar los procesos de adquisición de datos de calibración. A mayores, se presenta un exoesqueleto que es conectado a una arquitectura sensible al contexto con objeto de que los pacientes de rehabilitación hagan ejercicios de forma interactiva y haciendo uso de técnicas de realidad aumentada. En el último artículo, se hace hincapié en el diseño de una plataforma que hace uso de las redes inalámbricas de sensores, con objeto de monitorizar el estado de los aseos mediante la incorporación de agentes embebidos en dispositivos limitados computacionalmente. Esta información descentralizada es analizada con objeto de detectar posibles anomalías y facilitar la toma de decisiones. Uno de los principales hitos que se pretende con el estudio, es mostrar a la comunidad científica los diferentes resultados que se han obtenido en la investigación, solventando problemas cotidianos que han sido resueltos mediante la modelización de los casos de estudio mediante la utilización de arquitecturas multi-agente y sistemas expertos. El filtrado de señales, la utilización de clasificadores, minería de datos y la utilización de otras técnicas de Inteligencia Artificial han sido empleadas para la consecución exitosa de este trabajo

A Real-time Robust Indoor Tracking System in Smartphones

Nowadays, a growing number of ubiquitous mobile applications has increased the interest in indoor location-based services. Some indoor localization solutions for smartphones exploit radio information or data from Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this work, we propose to fuse WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter to achieve high accuracy and stable performance in the tracking process. Compared to our previous work, the improved stochastic model for location estimation is formulated in a discretized graph-based representation of the indoor environment. Additionally, we propose an efficient filtering approach for improving the IMU measurements, which is able to mitigate errors caused by inaccurate off-the-shelf IMUs and magnetic field disturbances. Moreover, we also provide a simple and efficient solution for localization failures like the kidnapped-robot problem. The tracking algorithms are designed in a terminal-based system, which consists of commercial smartphones and WiFi access points. We evaluate our system in a complex indoor environment. Results show that our tracking approach can automatically recover from localization failures, and it could achieve the average tracking error of 1.15 meters and a 90% accuracy of 1.8 meters