This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

A system and method for locating, tracking, and/or monitoring the status of personnel and/or assets (collectively “trackees”), both indoors and outdoors, is provided. Tracking data obtained from any number of sources utilizing any number of tracking methods may be provided as input to a mapping application. The mapping application generates position estimates for trackees using a suite of mapping tools to make corrections to the tracking data. The mapping application further uses information from building data, when available, to enhance position estimates. Indoor tracking methods including sensor fusion methods, map matching methods, and map building methods may be implemented compute a more accurate tracking estimate for trackees. Outdoor tracking methods may be implemented to enhance outdoor tracking data by combining tracking estimates such as inertial tracks with magnetic and/or compass data if and when available, and with GPS, if and when available.

System and method for locating, tracking, and/or monitoring the status of personnel and/or assets both indoors and outdoors

We introduce a novel self-contained seamless positioning solution for indoor and outdoor environments, well suited for and designed to be operated on off-the-shelf mobile phones. Position information is deduced from a combination of GNSS where available, combined with Pedestrian Dead Reckoning (PDR) utilizing inertial measurements and context-aware activity based map matching. The proposed system heavily exploits different types of human movement, such as walking, running, ascending or descending stairs, to improve the employed positioning model. In remaining independent from any external infrastructure, accurate localization is also possible in environments, where the installation and maintenance of such infrastructure does not make sense or is simply not affordable - as for example in a parking garage to guide a user to the next exit or back to his car. Concerning this particular use case we have also implemented an interface for the synchronization of location information between the mobile positioning solution and the car.

Self-contained indoor positioning on off-the-shelf mobile devices

The present invention relates to a method for positioning a user inside a building, wherein the user has a user carried device (100) and the user carried device (100) is provided with a direction sensor (110) and a movement sensor (110). The method comprises the steps of providing the user carried device with a vector map (220) of the building, wherein the vector map (220) comprises vectors and nodes representing possible movement paths for the user in the building; determining a starting point in the vector map representing a first position of the user; receiving movement information from the movement sensor (110) regarding movement of the user in the building; receiving direction information from the direction sensor (110) regarding the direction of the movement of the user; and estimating a new position of the user based on the vector map, the movement information and the direction information.

Method and device for indoor positioning

A mobile device can send a request to a server having a plurality of tiles of location data associated with a venue, each tile having location data associated with a portion of the venue, the request including data representing an estimated location of the mobile device. The mobile device can receive a first tile of location data sent from the server, the first tile being associated with a first portion of the venue that includes the estimated location. The mobile device can receive a first sensor reading and determine a first location of the mobile device at the venue using the first sensor reading and the first tile of location data. The mobile device may receive a second tile of location data sent from the server, the second tile being associated with a second portion of the venue. The mobile device may receive a second sensor reading and determine a second location of the mobile device at the venue using the second sensor reading and the second tile of location data.

Tiling of map data

The personal positioning and navigation became a very challenging topic in our dynamic time. The urban canyons and particularly indoors represent the most difficult areas for personal navigation problematic. Problems like disturbed satellite signals make the positioning impossible indoors. Recently developed systems for indoor positioning do not assure the necessary positioning accuracy or are very expensive. Our concept stands for a fully autonomous positioning and navigation process. That is, a method that does not rely on the reception of external information, like satellite or terrestrial signals. Therefore, this research is based on the use of inertial measurements of the human walk and the map database which contains the graphic representation of the elements of the building, created by applying the link-node model. Using this reduced set of information the task is to develop methodology, based on the interaction of the data from both sources, to assure reliable positioning and navigation process. This research is divided in three parts. The first part consists in the development of a methodology for initial localization of the person indoors. The problem to solve is to localize the person in the building. Consider a person equipped with a system which contains set of inertial sensors and map database of the building. Speed, turn rate and barometric altitude are measured and time-stamped on each step of the person. A pre-processing phase uses these raw measurements in order to construct a polyline, thus representing user's trajectory. In the localization approach central place takes the association of the user's trajectory with the graph representation of the building, process known as map-matching. The solution is based on statistical method where the determination of the user's position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Initial localization determines the edge of the graph occupied by the person. The second part aims at continuous localization, where user's position is estimated on every step. Besides the application of the classical map-matching techniques, two new methods are developed. Both rely on the similarity of the geometry of the trajectory and the elements of the graph. The first is based on the Bayesian inference, where the estimation is computed considering the walked distance and azimuth. The second method represents a new application of the Frechet distance as degree of similarity between two polylines. The third part is pointed at the pedestrian guidance. Once the user's position is known it is easy to compute the path to his destination and to give him directions. The problem is to assure continuance of the process of navigation in the case when the person has lost his path. In that case the solution consists in either giving instructions to the user to go back on the path or computation of a new path from the actual position of the user to his destination. Based on that methodology, algorithms for initial localization, continuous localization, and guidance were created. Numerous tests with the participation of several persons have been provided in order to validate the algorithms and to show their performance, robustness and limits.

Algorithms for map-aided autonomous indoor pedestrian positioning and navigation

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Algorithms for map-aided autonomous indoor pedestrian positioning and navigation