Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

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

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

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