Combined indoor/outdoor Smartphone navigation for public transport travellers (original) (raw)
STEPPING - Smartphone-Based Portable Pedestrian Indoor Navigation
Archiwum Fotogrametrii, Kartografii i Teledetekcji, 2011
Many current smartphones are fitted with GPS receivers, which, in combination with a map application form a pedestrian navigation system for outdoor purposes. However, once an area with insufficient satellite signal coverage is entered, these navigation systems cease to function. For indoor positioning, there are already several solutions available which are usually based on measured distances to reference points. These solutions can achieve resolutions as low as the sub-millimetre range depending on the complexity of the set-up. STEPPING project, developed at HCU Hamburg - Germany aims at designing an indoor navigation system consisting of a small inertial navigation system and a new, robust sensor fusion algorithm running on a current smartphone. As this system is theoretically able to integrate any available positioning method, it is independent of a particular method and can thus be realized on a smartphone without affecting user mobility. Potential applications include -- but a...
Pedestrian smartphone-based indoor navigation using ultra portable sensory equipment
2010 International Conference on Indoor Positioning and Indoor Navigation, 2010
Smartphones are largely benefiting from the ever increasing rate of integration of mobile IT components. Their increasing processing power and the fact that they are fitted with GNSS-receivers make them ideal for hand-held pedestrian navigation solutions. This document proposes a pedestrian indoor navigation system based on a custom inertial navigation system and the N900 smartphone made by Nokia. The system does not depend on a particular indoor navigation system, but attempts to support its inertial navigation system by selecting the optimal set of auxiliary information from available infrastructure such as WLAN and bluetooth. Analysis focuses on the development of the inertial navigation hardware, the definition of an optimal set of support information and the development of an efficient sensor fusion algorithm.
Performance Evaluation of Pedestrian Locations Based on Contemporary Smartphones
International Journal of Navigation and Observation, 2017
Nowadays, a Global Navigation Satellite System (GNSS) unit is embedded in nearly every smartphone. This unit allows a smartphone to detect the user’s location and motion, and it makes functions, such as navigation, tracking, and compass applications, available to the user. Therefore, the GNSS unit has become one of the most important features in modern smartphones. However, because most smartphones incorporate relatively low-cost GNSS chips, their localization accuracy varies depending on the number of accessible GNSS satellites, and it is highly dependent on environmental factors that cause interference such as forests and buildings. This research evaluated the performance of the GNSS units inside two different models of smartphones in determining pedestrian locations in different environments. The results indicate that the overall performances of the two devices were related directly to the environment, type of smartphone/GNSS chipset, and the application used to collect the infor...
Sensors (Basel, Switzerland), 2015
Pedestrian dead reckoning is a common technique applied in indoor inertial navigation systems that is able to provide accurate tracking performance within short distances. Sensor drift is the main bottleneck in extending the system to long-distance and long-term tracking. In this paper, a hybrid system integrating traditional pedestrian dead reckoning based on the use of inertial measurement units, short-range radio frequency systems and particle filter map matching is proposed. The system is a drift-free pedestrian navigation system where position error and sensor drift is regularly corrected and is able to provide long-term accurate and reliable tracking. Moreover, the whole system is implemented on a commercial off-the-shelf smartphone and achieves real-time positioning and tracking performance with satisfactory accuracy.
2007 4th Workshop on Positioning, Navigation and Communication, 2007
Context-enabled applications are more and more establishing on the market. A key feature for those tools is the ability to connect the current user state and position to possible activities nearby. Thus it is crucial to a mobile application to be able helping the tourists to find and guide them to appropriate activities. This paper will separately address the navigational component and evaluate the efficiency of several systems currently available. All of these systems are based on current state of the art approaches and are easily expandable to different locations meaning there isn't any adaptation needed, running the system in other cities. At the end the paper will come up with a recommendation for the most suited system to be integrated into mobile applications under current conditions.
Human-centred mobile Pedestrian navigation systems1)
2011
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APT: Accurate outdoor pedestrian tracking with smartphones
2013
This paper presents APT, a localization system for outdoor pedestrians with smartphones. APT performs better than the built-in GPS module of the smartphone in terms of accuracy. This is achieved by introducing a robust dead reckoning algorithm and an error-tolerant algorithm for map matching. When the user is walking with the smartphone, the dead reckoning algorithm monitors steps and walking direction in real time. It then reports new steps and turns to the mapmatching algorithm. Based on updated information, this algorithm adjusts the user's location on a map in an error-tolerant manner. If location ambiguity among several routes occurs after adjustments, the GPS module is queried to help eliminate this ambiguity. Evaluations in practice show that the error of our system is less than 1/2 that of GPS.
Last-Mile Navigation Using Smartphones
Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FOLLOWME, a "last-mile" navigation system to enable plugand-play navigation in indoor and semi-outdoor environments. FOLLOWME exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FOLLOWME is infrastructure-free, energyefficient and cost-saving. We implemented FOLLOWME on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m 2 . Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.
L5IN: Overview of an Indoor Navigation Pilot Project
Remote Sensing
While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason...
A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation
Micromachines, 2014
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. Since the use of none of the considered approaches (INS and RSS) can allow by itself obtaining a sufficiently good estimation error in the considered conditions of interest, a commonly adopted strategy to tackle this problem relies on the integration between the data collected by several types of sensors to achieve more robust localization results . In particular, 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) with measurements derived from the embedded INS sensor (the smartphone considered here is provided with a three-axis accelerometer and three-axis magnetometer). Furthermore, a map of the building is assumed to be available to the tracking algorithm: this information can be either provided by the Wi-Fi network or acquired by image plots or plans of the building.