Abstract
This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user’s point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user’s motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building.
Highlights
Thanks to certain socially relevant applications, indoor navigation is recently becoming a topic of wide interest among the research community
Three challenging aspects can be recognized in indoor navigation: the required accuracy is typically higher than in outdoor applications, the GPS signal is typically not available and there is low reliability of the sensor measurements (e.g., inertial navigation system (INS) measurements and RSS)
In this paper, an indoor navigation system with minimal positioning sensor equipment is considered: the goal of this work is to enable navigation with low-cost mobile devices in indoor and other critical environments, e.g., the proposed navigation algorithm can be executed on a smartphone, which estimates its own position inside a building by combining the information collected from the Wi-Fi network (RSS)
Summary
Thanks to certain socially relevant applications (e.g., localizing and tracking people inside buildings during emergencies), indoor navigation is recently becoming a topic of wide interest among the research community. In this paper, an indoor navigation system with minimal positioning sensor equipment is considered: the goal of this work is to enable navigation with low-cost mobile devices (typically carried by the user’s hand) in indoor and other critical environments, e.g., the proposed navigation algorithm can be executed on a smartphone, which estimates its own position inside a building by combining the information collected from the Wi-Fi network (RSS). From calibration algorithms typically used in similar systems [26,27,28], this calibration procedure does not require any specific action by the user: it is performed during the navigation, without affecting the user’s regular moves in any way This way, the adopted navigation procedure results in a new algorithm that integrates the positional information derived from INS and Wi-Fi RSS measurements with the geometrical information provided by the building map, while dealing with uncalibrated sensors, as well.
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