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

Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field

In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetometers. Instead of matching features such as walls and furniture using some variation of the ICP algorithm, the local magnetic field can be matched to provide loop-closure and global trajectory updates in a Gaussian Process (GP) SLAM framework. With a MEMS-based inertial measurement unit providing a continuous trajectory, and the matching of locally distinct magnetic field maps, experimental results in this paper show that a drift-free navigation solution in an indoor environment with millimetre-level accuracy can be achieved. The GP-SLAM approach presented can be formulated as a maximum a posteriori estimation problem and it can naturally perform loop-detection, feature-to-feature distance minimization, global trajectory optimization, and magnetic field map estimation simultaneously. Spatially continuous features (i.e. smooth magnetic field signatures) are used instead of discrete feature correspondences (e.g. point-to-point) as in conventional vision-based SLAM. These position updates from the ambient magnetic field also provide enough information for calibrating the accelerometer and gyroscope bias in-use. The only restriction for this method is the need for magnetic disturbances (which is typically not an issue indoors); however, no assumptions are required for the general motion of the sensor.Comment: ISPRS Workshop Indoor 3D 201

Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds

Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines

Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%