Jörg Blankenbach

Position estimation using artificial generated magnetic fields

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.

An IMU/magnetometer-based Indoor positioning system using Kalman filtering

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 systems 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.

Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet

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.

Building information systems based on precise indoor positioning

During the last 10 years, many modern IT-based applications have developed inside buildings. Many of those applications would benefit by the ability to locate people and/or objects inside the building (indoor positioning). However, most of todays indoor positioning systems are not able to deliver precise position information (<10 cm) along with quality parameters. Ultra wide band (UWB) is a new radio-based technology that allows the determination of distances in indoor environments with a very high spatial resolution even through building materials. At the Institute of Geodesy of TU Darmstadt, a high-resolution UWB positioning system (UWB-ILPS; ILPS, indoor local positioning systems) based on trilateration principle has been developed to estimate the position of a mobile station precisely. To benefit from knowing the position and orientation, it is necessary to select and merge data linked to the users location for indoor location services. By this means, the visitor to a public building may benefit from the system as his position is shown on a digital floor plan generated dynamically or by retrieving location-based information inside the building. Mixed reality systems also offer advantages for a mobile building information system. For this purpose, a webcam was replaced by the digital camera in the UWB-ILPS prototype. Knowing the cameras location in space and its view direction, one is able to merge the real world taken by the webcam with the virtual world represented by a 3D CAD model of the building.

Towards a robust smartphone-based 2,5D pedestrian localization

Automatic real-time localization of people inside buildings is a huge challenge. For demanding applications in building services, different sensors (e.g. WLAN, RFID, UWB, or ultrasound) are currently used for the real-time indoor positioning. Most systems are only suitable for specific applications or are used under certain conditions, e.g. additional infrastructures and/or sensory mechanisms are needed. Smartphones, as widespread low-cost multi sensor systems, seem to be a promising platform for mass-market indoor localization applications. In this paper we present an approach for a smartphone-based pedestrian positioning inside of buildings. The novelty of this approach refers to further investigations towards a robust smartphone-based 2.5D pedestrian localization inside of buildings. Therefore, comprehensive analyses concerning barometric height estimations were carried out to expand the predominant 2D position into the third dimension. The 2D position is estimated according to the principle of dead reckoning. Furthermore an extended algorithm based on Sequential Monte Carlo methods for sensor fusion is presented which also integrates building models and a magnetic indoor positioning system.

Enhancing the OGC WPS interface with GeoPipes support for real-time geoprocessing

ABSTRACT Real-time geospatial information is used in various applications such as risk management or alerting services. Especially, the rise of new sensing technologies also increases the demand for processing the data in real time. Today’s spatial data infrastructures, however, do not meet the requirements for real-time geoprocessing. The OpenGIS® Web Processing Service (WPS) is not designed to process real-time workflows. It has some major drawbacks in asynchronous processing and cannot handle (geo) data streams out of the box. In previous papers, we introduced the GeoPipes approach to share spatiotemporal data in real time. We implemented the concept extending the Message Queue and Telemetry Transport (MQTT) protocol by a spatial and temporal dimension, which we call GeoMQTT. In this paper, we demonstrate the integration of the GeoPipes idea in the WPS interface to expose standardized real-time geoprocessing services. The proof of the concept is illustrated in some exemplary real-time geo processes.

IMU/magnetometer based 3D indoor positioning for wheeled platforms in NLoS scenarios

In recent years the research on localization and navigation systems in GNSS-denied environments has been focused from both industry and research. Although many technologies based on e.g. UWB, WLAN, ultrasonic or infrared have been utilized, there is still no final solution for position and orientation determination in indoor areas. The fact, that applied signals in common approaches are influenced by fading and multipath inside buildings leads to restrictions on line-of-sight (LoS) conditions. In contrast, the ability of penetrating any kind of building materials qualifies magnetic fields to realize object positioning in harsh indoor environments. Hence, a DC Magnetic signal based Indoor Local Positioning System (MILPS) has been developed consisting of multiple electrical coils, representing reference stations. Based on the magnetic field intensities of at least three different coils, the corresponding slope distances and therefore the observers position can be estimated. Facing kinematic purposes a combination of MILPS and an Inertial Measurement Unit (IMU) has been applied, utilizing methods of sensor fusion. Observed inertial data - in this case three dimensional acceleration and angular rate measurements - lead to the sensors relative motion changes, which are processed by kinematic motion models. Based on a discrete integration with respect to the measurement time interval, the sensors current state - consisting of position, velocity and orientation - can be predicted. These high-frequency derived predictions are furthermore supported by external MILPS-distances and elevation angles utilizing methods of Kalman Filter. Focus in this contribution lies on the processing of both inertial data and magnetic field measurements for three dimensional applications.

Platform Architecture for Decentralized Positioning Systems

A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system.

GeoPipes using GeoMQTT

The integration of common OpenGIS Web Services (OWS) into the Internet of Things and Service (IoTS) paradigm is a difficult task since they are based on HTTP with all its weak points. E.g. coupling small sensing devices or real-time processes with these services takes an enormous effort due to the different domain requirements. This paper focuses on extending existing geo web services with a push-based messaging mechanism to overcome their major drawbacks. We introduce the concept of GeoPipes and an exemplary implementation of them using the GeoMQTT protocol. The latter one is an extension of the MQTT protocol which is presented in this paper. Application examples show that with this concept a lot of technological issues can be solved easier.

A novel method for overcoming the impact of spatially varying ambient magnetic fields on a DC magnetic field-based tracking system

A magnetic indoor local positioning system enables positioning in harsh environments due to the advantageous characteristics of magnetic signals such as penetrating of various obstacles and robustness against fading effects. Nevertheless, the indoor tracking remains a challenge, due to the non-stationary property of the ambient magnetic fields, signal transients and eddy current effects. In this paper, we propose a method, which is based on zero velocity detection, in order to overcome the mentioned constraints by the pedestrian tracking.