A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error.

Many infrastructure-based indoor positioning technologies such as UWB, WLAN, ultrasonic or infrared are limited by disturbances and errors caused by building objects (e.g. walls, ceiling and furniture). Magnetic fields, however, are able to penetrate various obstacles - in this case commonly used (building) materials - without attenuation, fading, multipath or signal delay. Thus, in the past years a DC Magnetic signal based Indoor Local Positioning System (MILPS), which consists of multiple electrical coils as reference stations and tri-axial magnetic sensors as mobile stations was developed. By observing magnetic field intensities of at least three different magnetic coils, position estimation of the magnetic sensors can be carried out even in severe indoor environments. However, the positioning algorithm currently used is designed for stop-and-go localization. This contribution focuses on the integration of a low cost Inertial Measurement Unit (IMU) in order to improve the system's positioning update rate and therefore provide complete 2D localization estimates for kinematic applications and probably afford position solutions even outside the coverage area of MILPS. Therefore an Extended Kalman-Filter (EKF) is adapted for position estimation. The filtering process is accomplished in two steps. The first step leads to position prediction caused by inertial data, which could be updated at the second step by using the MILPS-measurements. In this context simulations combining MILPS and IMU have been performed. Testing of the filter with real IMU-data and simulated MILPS positioning data delivered promising results for indoor positioning purposes.

A positioning system is introduced which overcomes the limitations of existing indoor positioning systems by the use of artificial quasi static magnetic fields. The proposed DC magnetic signals show no special multipath effects and have excellent characteristics for penetrating various obstacles. In this contribution the theory of coil-based magnetic fields as well as the basic function principle of the positioning system are described. Furthermore, a prototype that is currently under development is presented.

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.

In recent years there has been a considerable research on the development of indoor positioning systems. Several kinds of technologies such as ultrasonic, UWB, WLAN, optical waves and hybrid solutions were utilized already. However, using these technologies many difficulties arise in indoor environments due to none line of sight (NLoS) and multipath errors. In this paper, the realization and the evaluation of a 3D indoor localization system, which is robust for harsh and NLoS environments is presented. The positioning system is Direct Current (DC) magnetic based, shows no multipath effects and has excellent characteristics for penetrating various obstacles. To eliminate additional interference fields (e.g. earth's magnetic field, electrical disturbances) a differential measurement principle and adaptive noise suppression algorithms are used. In the case of the deployment in smaller areas, even smart phones equipped with embedded low cost sensors can be utilized as mobile station. A real time 3D position estimation with an accuracy up to 50 cm is achievable by setting up only three magnetic coils inside or around the building. In order to analyze existing systematic errors, a simple calibration procedure has been implemented. The calibration routine reduces the systematic errors, which leads to improved system's positioning accuracy up to 10 cm.

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.