Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic

Mapeamento magnético para navegação robótica em ambientes interiores

Localization has always been one of the fundamental problems in the field of robotic navigation. The emergence of GPS came as a solution for localization systems in outdoor environments. However, the accuracy of GPS is not always sufficient and GPS based systems often fail and are not suited for indoor environments. Considering this, today there is a variety of real time localization technologies. It is quite common to see magnetic anomalies in indoor environments, which arise due to the presence of ferromagnetic objects, such as concrete or steel infrastructures. In the conventional ambient magnetic field based robotic navigation, which uses the direction of the Earth’s magnetic field to determine orientation, these anomalies are seen as undesirable. However, if the environment is rich in anomalies with sufficient local variability, they can be mapped and used as features for localization purposes. The work presented in this dissertation aims at demonstrating that it is possible to combine the odometric measurements of a mobile robot with magnetic field measurements, in order to effectively estimate the position of the robot in real time in an indoor environment. For this purpose, it is necessary to map the navigation space and develop a localization algorithm. First, the issues addressed to create a magnetic map are presented, namely data acquisition, employed interpolation methods and validation processes. Subsequently, the developed localization algorithm, based on a particle filter, is depicted, as well as the respective experimental validation tests.A localização sempre fui um dos problemas fundamentais a resolver no âmbito da navegação robótica. O surgimento do GPS veio a servir de solução para bastantes sistemas de localização em ambientes exteriores. No entanto, a exatidão do GPS nem sempre é suficiente e os sistemas baseados em GPS falham frequentemente e não são aplicáveis em ambientes interiores. À vista disso, hoje existe uma variedade de tecnologias de localização em tempo real. É bastante comum verificarem-se anomalias magnéticas em ambientes interiores, que provêm de objetos ferromagnéticos, como infraestruturas de betão ou aço. Na navegação robótica baseada na leitura do campo magnético convencional, que utiliza a direção do campo magnético terrestre para determinar a orientação, estas anomalias são vistas como indesejáveis. No entanto, se o ambiente for rico em anomalias com variabilidade local suficiente, estas podem ser mapeadas e utilizadas como caraterísticas para efeitos de localização. O trabalho apresentado nesta dissertação visa a demonstrar que é possível conjugar as medidas odométricas de um robô móvel com medições do campo magnético, para efetivamente localizar o robô em tempo real num ambiente interior. Para esse efeito, é necessário mapear o espaço de navegação e desenvolver um algoritmo de localização. Primeiramente, são apresentadas as questões abordadas para criar um mapa magnético, nomeadamente as aquisições de dados, os métodos de interpolação e os processos de validação. Posteriormente, é retratado o algoritmo de localização desenvolvido, baseado num filtro de partículas, assim como os respetivos testes experimentais de validação.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Localization Algorithms for GNSS-denied and Challenging Environments

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as fingerprint matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the accuracy of the estimated localization is affected by the moving objects when performing matching. In this dissertation, two sensor fusion based strategies are proposed to solve localization problems with respect to these two types of challenging environments, respectively. For environments with only low-resolution features, such as flying over sea or desert, a multi-agent localization algorithm using pairwise communication with ranging and magnetic anomaly measurements is proposed in this dissertation. A scalable framework is then presented to extend the multi-agent localization algorithm to be suitable for a large group of agents (e.g., 128 agents) through applying CI algorithm. The simulation results show that the proposed algorithm is able to deal with large group sizes, achieve 10 meters level localization performance with 180 km traveling distance, while under restrictive communication constraints. For environments including moving objects, lidar-inertial-based solutions are proposed and tested in this dissertation. Inspired by the CI algorithm presented above, a potential solution using multiple features motions estimate and tracking is analyzed. In order to improve the performance and effectiveness of the potential solution, a lidar-inertial based SLAM algorithm is then proposed. In this method, an efficient tightly-coupled iterated Kalman filter with a build-in dynamic object filter is designed as the front-end of the SLAM algorithm, and the factor graph strategy using a scan context technology as the loop closure detection is utilized as the back-end. The performance of the proposed lidar-inertial based SLAM algorithm is evaluated with several data sets collected in environments including moving objects, and compared with the state-of-the-art lidar-inertial based SLAM algorithms

Навігація БПЛА в приміщенні на основі TDOA методу

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: професор сумісник кафедри авіоніки, Сібрук Леонід ВікторовичThe popularity of UAV’s during last years is greatly increasing. Drones are getting more broad use in various commercial applications. They are used for mapping, monitoring, logistics, media, search and rescue operations and many more possible use cases. One of the recently emerged UAV’s type are indoor drones. Such drones are mostly used for inspections, security monitoring, warehouse operations and public safety. On this basis, a demand for indoor navigation system arises. The specifics of indoor operations of drones, creates unique technical challenges. Development of reliable and precise navigational systems, will allow to implement autonomous UAV system, which will vastly increase efficiency of indoor drone operations. Studies on this topic are sparse and require further investigations and development. For development of navigation systems, it is possible to rely on existing technologies from different areas, such as indoor positioning for pedestrian navigation, or positioning algorithms, used in aviation. Estimation of theoretical performance and accuracy of indoor navigational algorithms and technologies can allow further improvements and implementation of new technologies for practical use. The developed mathematical model is used for analysis of TDOA-based positioning algorithm, which can be used in such positioning systems.Популярность БПЛА в последние годы значительно возрастает. Дроны получают все более широкое применение в различных коммерческих приложениях. Они используются для картирования, мониторинг, логистика, средства массовой информации, поисково-спасательные операции и многое другое возможное использование случаи. Одним из недавно появившихся типов БПЛА являются внутренние дроны. Такие дроны чаще всего используется для инспекций, мониторинга безопасности, складских операций и общественной безопасности. На этом основе возникает спрос на внутреннюю навигационную систему. Специфика работы внутри помещений дронов, создает уникальные технические проблемы. Разработка надежных и точных навигационных систем, позволит реализовать автономную систему БПЛА, что значительно повысить эффективность работы дронов внутри помещений. Исследования по этой теме немногочисленны и требуют дальнейших исследований и разработок. Для разработки навигационных систем можно полагаться на существующие технологии от различные области, такие как позиционирование в помещении для пешеходной навигации или позиционирование алгоритмы, используемые в авиации. Оценка теоретической производительности и точности алгоритмов внутренней навигации и технологии могут позволить дальнейшие улучшения и внедрение новых технологий для практического использования. Разработанная математическая модель используется для анализа алгоритм позиционирования, который можно использовать в таких системах позиционирования

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science

Indoor Localization Using Magnetic Fields

Indoor localization consists of locating oneself inside new buildings. GPS does not work indoors due to multipath reflection and signal blockage. WiFi based systems assume ubiquitous availability and infrastructure based systems require expensive installations, hence making indoor localization an open problem. This dissertation consists of solving the problem of indoor localization by thoroughly exploiting the indoor ambient magnetic fields comprising mainly of disturbances termed as anomalies in the Earth’s magnetic field caused by pillars, doors and elevators in hallways which are ferromagnetic in nature. By observing uniqueness in magnetic signatures collected from different campus buildings, the work presents the identification of landmarks and guideposts from these signatures and further develops magnetic maps of buildings - all of which can be used to locate and navigate people indoors. To understand the reason behind these anomalies, first a comparison between the measured and model generated Earth’s magnetic field is made, verifying the presence of a constant field without any disturbances. Then by modeling the magnetic field behavior of different pillars such as steel reinforced concrete, solid steel, and other structures like doors and elevators, the interaction of the Earth’s field with the ferromagnetic fields is described thereby explaining the causes of the uniqueness in the signatures that comprise these disturbances. Next, by employing the dynamic time warping algorithm to account for time differences in signatures obtained from users walking at different speeds, an indoor localization application capable of classifying locations using the magnetic signatures is developed solely on the smart phone. The application required users to walk short distances of 3-6 m anywhere in hallway to be located with accuracies of 80-99%. The classification framework was further validated with over 90% accuracies using model generated magnetic signatures representing hallways with different kinds of pillars, doors and elevators. All in all, this dissertation contributes the following: 1) provides a framework for understanding the presence of ambient magnetic fields indoors and utilizing them to solve the indoor localization problem; 2) develops an application that is independent of the user and the smart phones and 3) requires no other infrastructure since it is deployed on a device that encapsulates the sensing, computing and inferring functionalities, thereby making it a novel contribution to the mobile and pervasive computing domain

Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Multiple Model-based Indoor Localization via Bluetooth Low Energy and Inertial Measurement Unit Sensors

Ubiquitous presence of smart connected devices coupled with evolution of Artificial Intelligence (AI) within the field of Internet of Things (IoT) have resulted in emergence of innovative ambience awareness concepts such as smart homes and smart cities. In particular, IoT-based indoor localization has gained significant popularity, given the expected widespread implementation of 5G network, to satisfy the ever increasing requirements of Location-based Services (LBS) and Proximity Based Services (PBS). LBSs and PBSs have found several applications under different circumstances such as localization profiling for human resource management; navigation assistant applications in smart buildings/hospitals, and; proximity based advertisement and marketing. The focus of this thesis is, therefore, on design and implementation of efficient and accurate indoor localization processing and learning techniques. In particular, the thesis focuses on the following three positioning frameworks: (i) \textit{Bluetooth Low Energy (BLE)-based Indoor Localization}, which uses the pathloss model to estimate the user's location; (ii) \textit{Inertial Measurement Unit (IMU)-based Indoor Positioning}, where smart phone's $3$ axis inertial sensors are utilized to iteratively estimate the headings and steps of the target, and; (iii) \textit{Pattern Recognition-based Indoor Localization}, which uses Deep Neural Networks (DNNs) to estimate the performed actions and find the user's location. With regards to Item (i), the thesis evaluates effects of the orientation of target's phone, Line of Sight (LOS) / Non Line of Sight (NLOS) signal propagation, and presence of obstacles in the environment on the BLE-based distance estimates. Additionally, a fusion framework, combining Particle Filtering with K-Nearest Neighbors (K-NN) algorithm, is proposed and evaluated based on real datasets collected through an implemented LBS platform. With regards to Item (ii), an orientation detection and multiple-modeling framework is proposed to refine Received Signal Strength Indicator (RSSI) fluctuations by compensating negative orientation effects. The proposed data-driven and orientation-free modeling framework provides improved localization results. With regards to Item (iii), the focus is on classifying actions performed by a user using Long Short Term Memory (LSTM) architectures. To address issues related to cumulative error of Pedestrian Dead Reckoning (PDR) solutions, three Online Dynamic Window (ODW) assisted LSTM positioning frameworks are proposed. The first model, uses a Natural Language Processing (NLP)-inspired Dynamic Window (DW) approach, which significantly reduces the computation time required for Real Time Localization Systems (RTLS). The second framework is developed based on a Signal Processing Dynamic Window (SP-DW) approach to further reduce the required processing time of the two stage LSTM based indoor localization. The third model, referred to as the SP-NLP, combines the first two models to further improve the overall achieved accuracy