In the last decade, many efforts have been devoted to indoor localization and positioning. In this paper, a hybrid indoor localization system has been developed within the European project REFIRE for emergency situations. The REFIRE solution estimates the user's pose according to a prediction-correction scheme. The user is equipped with a waist-mounted inertial measurement unit and a radio frequency identification (RFID) reader. In the correction phase, the estimation is updated by means of geo-referenced information fetched from passive RFID tags predeployed into the environment. Accurate position correction is obtained through a deep analysis of the RFID system radiation patterns. To this end, extensive experimental trials have been performed to assess the RFID system performance, both in static and dynamic operating conditions. Experimental validation in realistic environments shows the effectiveness of the proposed indoor localization system, even during long-last missions and/or using a limited number of tags.

We present an innovative smartphone-centric tracking system for indoor and outdoor environments, based on the joint utilization of dead-reckoning and computer vision (CV) techniques. The system is explicitly designed for visually impaired people (although it could be easily generalized to other users) and it is built under the assumption that special reference signals, such as painted lines, colored tapes or tactile pavings are deployed in the environment for guiding visually impaired users along pre-defined paths. Thanks to highly optimized software, we are able to execute the CV and sensor-fusion algorithms in run-time on low power hardware such as a normal smartphone, precisely tracking the users movements.

In this paper a low cost solution for implementing an indoor localization system is proposed. The Android app is based on a background service able to log data retrieved from the 9-Degree of Freedom IMU embedded in a smartphone and to compute the current user position. In particular, an Android application is developed: it is able to track the position of a user in indoor environment using smartphones embedded inertial sensors. The estimated position is shown on a map in a graphic user interface. This system has been developed for rescuers and building maintenance workers and it is able to track user in a planar environment. The key idea is to obtain a cheap solution still able to guarantee the room level accuracy. Experimental tests show the effectiveness of the proposed solution.

Localization and tracking are fundamental features in emergency response operations, where the mission leader needs to be aware of the team location. This paper addresses the localization for rescuers by exploiting wireless sensor networks embedded in the environment. Specifically, a pre-deployed network is considered and a localization algorithm is designed to find the location of the node and to track the rescuers cooperatively. Nodes estimate their own positions, while rescuers improve and augment their location awareness during mission by navigating across via points suggested by the network, thus improving the overall localizability. Experimental results show the effectiveness of the approach.

In this paper, an algorithm to estimate the position of a pedestrian in a 3-dimensional space is introduced. The proposed algorithm exploits the data provided by a waist-worn inertial platform and does not rely on the presence of any external infrastructure. Relevant features are extracted from the accelerometer data and are used to detect pedestrian activities such as standing, walking, going upstairs, or going downstairs. The estimate of the position is updated through a step detection procedure, which combines the signals provided by the inertial platform with the information about the pedestrian activity class.

Localization and tracking support is useful in many contexts and becomes crucial in emergency response scenarios: being aware of team location is one of the most important knowledge for incident commander. In this work both localization and tracking for rescuers are addressed in the framework of REFIRE project. The designed positioning system is based on the well-known prediction-correction schema adopted in field robotics. Proprioceptive sensors, i.e., inertial sensors and magnetometer, mounted on the waist of the rescuers, are used to form a coarse estimation of the locations. Due to the drift of inertial sensors, the position estimate needs to be updated by exteroceptive sensors, i.e., RFID system composed by tags embedded in the emergency signs as exteroceptive sensors and a wearable tag-reader. In long-lasting mission RFID tags reset the drift by providing a positioning having room-level accuracy.