Indoor pedestrian dead reckoning calibration by visual tracking and map information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data

MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications

First responders occupancy, activity and vital signs monitoring - SAFESENS

This paper describes the development and implementation of the SAFESENS (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) location tracking and first responder monitoring demonstrator. An international research collaboration has developed a stateof-the-art wireless indoor location tracking system for first responders, focused initially on fire fighter monitoring. Integrating multiple gas sensors and presence detection technologies with building safety sensors and personal monitors has resulted in more accurate and reliable fire and occupancy detection information. This is invaluable to firefighters in carrying out their duties in hostile environments. This demonstration system is capable of tracking occupancy levels in an indoor environment as well as the specific location of fire fighters within those buildings, using a multi-sensor hybrid tracking system. This ultra-wideband indoor tracking system is one of the first of itsâ kind to provide indoor localization capability to sub meter accuracies with combined Bluetooth low energy capability for low power communications and additional inertial, temperature and pressure sensors. This facilitates increased precision in accuracy detection through data fusion, as well as the capability to communicate directly with smartphones and the cloud, without the need for additional gateway support. Glove based, wearable technology has been developed to monitor the vital signs of the first responder and provide this data in real time. The helmet mounted, wearable technology will also incorporate novel electrochemical sensors which have been developed to be able to monitor the presence of dangerous gases in the vicinity of the firefighter and again to provide this information in real time to the fire fighter controller. A SAFESENS demonstrator is currently deployed in Tyndall and is providing real time occupancy levels of the different areas in the building, as well as the capability to track the location of the first responders, their health and the presence of explosive gases in their vicinity. This paper describes the system building blocks and results obtained from the first responder tracking system demonstrator depicted

Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding

Characterizing Power Consumption of Dual-Frequency GNSS of a Smartphone

Location service is one of the most widely used features on a smartphone. More and more apps are built based on location services. As such, demand for accurate positioning is ever higher. Mobile brand Xiaomi has introduced Mi 8, the world's first smartphone equipped with a dual-frequency GNSS chipset which is claimed to provide up to decimeter-level positioning accuracy. Such unprecedentedly high location accuracy brought excitement to industry and academia for navigation research and development of emerging apps. On the other hand, there is a significant knowledge gap on the energy efficiency of smartphones equipped with a dual-frequency GNSS chipset. In this paper, we bridge this knowledge gap by performing an empirical study on power consumption of a dual-frequency GNSS phone. To the best our knowledge, this is the first experimental study that characterizes the power consumption of a smartphone equipped with a dual-frequency GNSS chipset and compares the energy efficiency with a single-frequency GNSS phone. We demonstrate that a smartphone with a dual-frequency GNSS chipset consumes 37% more power on average outdoors, and 28% more power indoors, in comparison with a singe-frequency GNSS phone.Comment: Published in IEEE Global Communications Conference (GLOBECOM

SysMART Indoor Services: A System of Smart and Connected Supermarkets

Smart gadgets are being embedded almost in every aspect of our lives. From smart cities to smart watches, modern industries are increasingly supporting the Internet of Things (IoT). SysMART aims at making supermarkets smart, productive, and with a touch of modern lifestyle. While similar implementations to improve the shopping experience exists, they tend mainly to replace the shopping activity at the store with online shopping. Although online shopping reduces time and effort, it deprives customers from enjoying the experience. SysMART relies on cutting-edge devices and technology to simplify and reduce the time required during grocery shopping inside the supermarket. In addition, the system monitors and maintains perishable products in good condition suitable for human consumption. SysMART is built using state-of-the-art technologies that support rapid prototyping and precision data acquisition. The selected development environment is LabVIEW with its world-class interfacing libraries. The paper comprises a detailed system description, development strategy, interface design, software engineering, and a thorough analysis and evaluation.Comment: 7 pages, 11 figur