Federica Inderst

An enhanced indoor positioning system for first responders

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.

Indoor positioning system using walking pattern classification

In the age of automation the ability to navigate persons and devices in indoor environments has become increasingly important for a rising number of applications. While Global Positioning System can be considered a mature technology for outdoor localization, there is no off-the-shelf solution for indoor tracking. In this contribution, an infrastructure-less Indoor Positioning System based on walking feature detection is presented. The proposed system relies on the differences characterizing different human actions (e.g., walking, ascending or descending stairs, taking the elevator). The motion features are extracted in time domain by exploiting data provided by a 9DoF Inertial Measurement Unit. The positioning algorithm is based on walking distance and heading estimation. Step count and step length are used to determine the walking distance, while the heading is computed by quaternions. An experimental setup has been developed. The collected results show that system guarantee room level accuracy during long trials.

Hybrid map building for personal indoor navigation systems

Tracking the positions of people in large indoor spaces is important, since it enables a range of applications related to security, indoor navigation and guidance. This paper proposes a personal indoor navigation system based on hybrid map, containing geometric as well as symbolic information. In this way the same map can be exploited to guide and localise the user efficiently during navigation. The hybrid map is built using floor plans of the environment. It is a topological graph capturing the connectivity of complex indoor environment and it is retrieved by applying image-processing techniques. Some additional metric information are added to make the map suitable for quantitative localisation. Semantic features are considered to improve user readability.

3D pedestrian dead reckoning and activity classification using waist-mounted inertial measurement unit

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.

Improving Situational Awareness for First Responders

This paper aims at exploring a novel approach for indoor localisation by exploiting data fusion. Specifically, personnel localisation in rescue scenarios is addressed: the key idea is to increase the situation awareness of rescuers. A pedestrian dead reckoning algorithm based on waist mounted inertial sensors is designed to cope with different human activities. The drifting estimate is re-calibrated by using information gathered from the environment. The outcomes of experimental trials performed in a real scenario are reported.

C-IPS: A smartphone based Indoor Positioning System

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.

Faulty or Malicious Anchor Detection Criteria for Distance-Based Localization.

The reliability of the localization of Wireless Sensor Networks in presence of errors or malicious data alteration is a challenging research topic: recently, several studies have been carried out to identify, remove or neglect the faulted/malicious nodes. This paper addresses the capability of a network, composed of range-capable nodes and anchor nodes (i.e., nodes that know their position), to detect a faulty or malicious alteration of the information provided by the anchor nodes. Specifically, we consider biases for the position of anchor nodes that alter the localization of the network, and we provide conditions under which the nodes are able to detect the event, with particular reference to two distance-based localization algorithms, namely trilateration and Shadow Edge Localization Algorithm.

Demo: Sensor Fusion Localization and Navigation for Visually Impaired People

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.

Hybrid Indoor Positioning System for First Responders

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.

Improving the Safety and the Operational Efficiency of Emergency Operators via On-field Situational Awareness

In rescue missions, the situational awareness represents an essential tool in supporting rescue team operating in unknown and complex indoor environments. In case of fire in highly congested industrial scenarios (e.g., refineries, oil depots, petrochemical plants, etc.), the smoke may reduce the awareness of the rescuer about potential local resources/hazards, affecting both operational efficiency and personal safety. The mitigation of potential consequences arising from major accidents can be limited providing the emergency staff with tools able to foster their role on field. In this paper, we present the RISING (indooR localizatIon and building maintenance using radio frequency Identification and inertial NaviGation) project that is devoted to support on field operators supplying them with a system for situational awareness and personal indoor positioning. The RISING solution is based on the integration of the RFID technology with the inertial navigation. A set of RFID tags, conveniently preinstalled in the working environment, can store information about their absolute position and the site of local items. This information can be easily retrieved on-the-fly using RFID readers and displayed on smart devices with which the user is equipped (e.g., tablet and/or smartphone) to allow on field situational awareness.