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

Magnetic Field Feature Extraction and Selection for Indoor Location Estimation

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios

Uso del campo magnético de la tierra para localizar a las personas en interiores

The location of an individual is a fundamental element of information for some commercial and assistive locationbased applications. Since the global positioning system, the most effective technology for positioning a mobile object in the outdoors does not work in indoor environments, several technological approaches have been proposed to tackle this problem. In this direction, in this paper we present an interesting approach based on the use of earth magnetic-field variations to estimate the localization of an individual in indoor environments

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set

UJIIndoorLoc-Mag: A New Database for Magnetic Field-Based Localization Problems

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 13-16 October 2015, Banff, Albeta, CanadaIndoor localization is a key topic for mobile computing. However, it is still very difficult for the mobile sensing community to compare state-of-art Indoor Positioning Systems due to the scarcity of publicly available databases. Magnetic field-based methods are becoming an important trend in this research field. Here, we present UJIIndoorLoc-Mag database, which can be used to compare magnetic field-based indoor localization methods. It consists of 270 continuous samples for training and 11 for testing. Each sample comprises a set of discrete captures taken along a corridor with a period of 0.1 seconds. In total, there are 40,159 discrete captures, where each one contains features obtained from the magnetometer, the accelerometer and the orientation sensor of the device. The accuracy results obtained using two baseline methods are also presented to show the suitability of the presented database for further comparisons

MagSLAM: Aerial Simultaneous Localization and Mapping using Earth\u27s Magnetic Anomaly Field

Instances of spoofing and jamming of global navigation satellite systems (GNSSs) have emphasized the need for alternative navigation methods. Aerial navigation by magnetic map matching has been demonstrated as a viable GNSS‐alternative navigation technique. Flight test demonstrations have achieved accuracies of tens of meters over hour‐long flights, but these flights required accurate magnetic maps which are not always available. Magnetic map availability and resolution vary widely around the globe. Removing the dependency on prior survey maps extends the benefits of aerial magnetic navigation methods to small unmanned aerial systems (sUAS) at lower altitudes where magnetic maps are especially undersampled or unavailable. In this paper, a simultaneous localization and mapping (SLAM) algorithm known as FastSLAM was modified to use scalar magnetic measurements to constrain a drifting inertial navigation system (INS). The algorithm was then demonstrated on real magnetic navigation flight test data. Similar in performance to the map‐based approach, MagSLAM achieved tens of meters accuracy in a 100‐minute flight without the use of a prior magnetic map. Aerial SLAM using Earth\u27s magnetic anomaly field provides a GNSS‐alternative navigation method that is globally persistent, impervious to jamming or spoofing, stealthy, and locally accurate to tens of meters without the need for a magnetic map

Yeni Bir İç Mekân Konum Bulma Sistemi

Teknolojinin gelişimine paralel olarak konum bulma sistemleri çok önem kazanmıştır. Konum bulma sistemleri dış mekân konum bulma sistemleri ve iç mekân konum bulma sistemleri diye temel olarak iki sınıf altında toplanmıştır. Dış mekân konum bulma sistemleri genellikle GPS sinyallerini temel aldığı için iç ortamda verimli bir şekilde çalışmamaktadır. İç mekân konum bulma sistemleri gelişimini sürdürmektedir ve üzerine birçok çalışma yapılmaktadır. Bu çalışmada, verimli bir iç mekan konum bulma sistemi oluşturabilmek için elektronik kart tasarımları gerçekleştirilmiştir. İç mekân konum bulma yöntemi için bu kartlara uygun yazılımlar hazırlanmıştır. Ayrıca sistemi verimli bir şekilde analiz edebilmek için arayüz programı tasarlanmıştır. Yapılan test sonuçları ve veriler incelendiğinde iç mekân konum bulma sistemi için uygun maliyet ile yakın doğruluk değerlerine ulaşıldığı gözlemlenmiştir

A Study on Indoor Positioning using 3-Dimensionalization Geomagnetic Fingerprint

Indoor positioning based on geomagnetism has been actively studied because of the stable signal and high resolution positioning accuracy even when the time has elapsed. Because the geomagnetic signal can vary according to changes in azimuth, large positioning errors may occur, even from the same position. Therefore, this thesis proposes a fingerprint-based indoor positioning algorithm that fuses 2-Dimensional magnetic vectors and yaw-axis correction techniques. In the proposed 3-Dimensional system, the curvature is less biased heavily by using the Ellipse Coefficient Map of the geomagnetism based on the normalized linear least squares method even when database size is reduced, and the accuracy of positioning is improved by applying the geomagnetic signal equalization method. To verify the validity of the proposed algorithm in general indoor spaces of 48m × 30m, the results of the proposed method are compared with results obtained existing research based on geomagnetism intensity. The results show that the positioning accuracy is improved by 62.14% and the error distance is reduced by 3.98m.|지자기기반 실내위치인식은 시간이 경과되더라도 안정적인 신호 및 높은 분해능으로 측위 정확성이 높기 때문에 활발히 연구되고 있다. 그러나 동일한 위치에서도 방위 변화에 따른 지자기 신호가 일정하지 않기 때문에 위치 오차가 발생한다. 본 논문에서는 Fingerprint기반 2차원 자기벡터 및 yaw축 보정을 적용한 실내위치인식 알고리즘을 제안한다. 제안한 3차원화 시스템은 정규화 선형 최소자승법을 적용한 지자기의 Ellipse Coefficient Map을 설계함으로써 데이터베이스가 간소화됨에도 곡률편향이 거의 없고 지자기 신호 평활화 기법을 적용함으로써 위치인식 정확도를 향상시켰다. 제안한 알고리즘의 타당성을 검증하기 위하여 48m × 30m의 일반적인 실내공간에서 기존 지자기 세기기반 방식과 제안한 방법에 대한 결과를 비교 및 분석하였다. 실험 결과 위치 인식 정확도는 62.14% 개선하였고 오차거리는 3.98m 감소하였다.Abstract ⅳ 제 1 장 서 론 01 제 2 장 관련이론 06 2.1 지구자기장 06 2.2 자기벡터기반 방위각 획득 07 2.3 최소자승법 11 2.4 Fingerprint 측위 기법 13 제 3 장 제안한 실내 위치 인식 방법 15 3.1 시스템 구조 15 3.2 3차원화 Training phase 16 3.3 3차원화 Positioning phase 20 제 4 장 실험 및 결과 23 4.1 실험 환경 23 4.2 실험 결과 분석 27 제 5 장 결 론 38 참 고 문 헌 39Maste