Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Indoor Localization for Fire Safety : A brief overview of fundamentals, needs and requirements and applications

An indoor localization system for positioning evacuating people can be anticipated to increase the chances of a safe evacuation and effective rescue intervention in case of a tunnel fire. Such a system may utilize prevalent wireless technologies, e.g., Bluetooth, RFID and Wi-Fi, which today are used to survey incoming and outgoing traffic to a certain space or location, to estimate group sizes and to measure the duration of visits during normal operation of buildings. Examples also exist of where the same wireless technologies are used for safety purposes, for example to assess real-time location, tracking and monitoring of vehicles, personnel and equipment in mining environments. However, they are relatively few, and typically rely on a high degree of control over the people that are to be tracked, and their association with (connection to) the localization system used for the tracking. In this report, the results of a brief overview of the literature within the field of indoor localization in general, and the application of indoor localization systems within the field of particularly fire safety, is summarized. This information forms the underlying basis for the planning and execution of a future field study, in which an indoor Wi-Fi localization system will be tested and evaluated in terms of if, and if so how, it can be used to position evacuating people in tunnels. Whereas such a system allows digital footprints to be collected within a wireless network infrastructure (also already existing ones), questions remains to be answered regarding aspects such as precision and accuracy, and furthermore, how these aspects are affected by other independent variables. In the end of this report, examples of research questions deemed necessary to answer in order to enable a sound evaluation of the system is presented. These need to be addressed in the future planning of the above-mentioned field study

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

Nowadays, mobile phones are more than a device that can only satisfy the communication need between people. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. Fisheye lens equipment used with mobile phones was tested in this study. For this, standard calibration of ‘Olloclip 3 in one’ fisheye lens used with iPhone 4S mobile phone and ‘Nikon FC‐E9’ fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses. The dimensions of fisheye lenses used with smart phones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

Localization and 3D Reconstruction of Urban Scenes Using GPS

Using off-the-shelf Global Positioning System (GPS) units, we reconstruct buildings in 3D by exploiting the reduction in signal to noise ratio (SNR) that occurs when the buildings obstruct the line-of-sight between the moving units and the orbiting satellites. We measure the size and height of skyscrapers as well as automatically constructing a density map representing the location of multiple buildings in an urban landscape. If deployed on a large scale, via a cellular service provider’s GPS-enabled mobile phones or GPS-tracked delivery vehicles, the system could provide an inexpensive means of continuously creating and updating 3D maps of urban environments

The always best positioned paradigm for mobile indoor applications

In this dissertation, methods for personal positioning in outdoor and indoor environments are investigated. The Always Best Positioned paradigm, which has the goal of providing a preferably consistent self-positioning, will be defined. Furthermore, the localization toolkit LOCATO will be presented, which allows to easily realize positioning systems that follow the paradigm. New algorithms were developed, which particularly address the robustness of positioning systems with respect to the Always Best Positioned paradigm. With the help of this toolkit, three example positioning-systems were implemented, each designed for different applications and requirements: a low-cost system, which can be used in conjunction with user-adaptive public displays, a so-called opportunistic system, which enables positioning with room-level accuracy in any building that provides a WiFi infrastructure, and a high-accuracy system for instrumented environments, which works with active RFID tags and infrared beacons. Furthermore, a new and unique evaluation-method for positioning systems is presented, which uses step-accurate natural walking-traces as ground truth. Finally, six location based services will be presented, which were realized either with the tools provided by LOCATO or with one of the example positioning-systems.In dieser Doktorarbeit werden Methoden zur Personenpositionierung im Innen- und Außenbereich von Gebäuden untersucht. Es wird das ,,Always Best Positioned” Paradigma definiert, welches eine möglichst lückenlose Selbstpositionierung zum Ziel hat. Weiterhin wird die Lokalisierungsplattform LOCATO vorgestellt, welche eine einfache Umsetzung von Positionierungssystemen ermöglicht. Hierzu wurden neue Algorithmen entwickelt, welche gezielt die Robustheit von Positionierungssystemen unter Berücksichtigung des ,,Always Best Positioned” Paradigmas angehen. Mit Hilfe dieser Plattform wurden drei Beispiel Positionierungssysteme entwickelt, welche unterschiedliche Einsatzgebiete berücksichtigen: Ein kostengünstiges System, das im Zusammenhang mit benutzeradaptiven öffentlichen Bildschirmen benutzt werden kann; ein sogenanntes opportunistisches Positionierungssystem, welches eine raumgenaue Positionierung in allen Gebäuden mit WLAN-Infrastruktur ermöglicht, sowie ein metergenaues Positionierungssystem, welches mit Hilfe einer Instrumentierung aus aktiven RFID-Tags und Infrarot-Baken arbeitet. Weiterhin wird erstmalig eine Positionierungsevaluation vorgestellt, welche schrittgenaue, natürliche Bewegungspfade als Referenzsystem einsetzt. Im Abschluss werden 6 lokationsbasierte Dienste vorgestellt, welche entweder mit Hilfe von LOCATO oder mit Hilfe einer der drei Beispiel-Positionierungssysteme entwickelt wurden

Design and implementation of an indoor modeling method through crowdsensing

While automatic modeling and mapping of outdoor environments is well-established, the indoor equivalent of automated generation of building floor plans poses a challenge. In fact, outdoor localization is commonly available and inexpensive through the existing satellite positioning systems, such as GPS and Galileo. However, these technologies are not applicable in indoor environments, since a direct line of sight to the satellites, orbiting the globes, is required. As a substitution, the technical literature comprises several proposals for the development of simultaneous indoor localization and mapping (SLAM). In these approaches, the authors mostly exploit indoor resources such as the WiFi access points and the mobile smart devices carried by individuals in the indoor environment. Collecting data from several mobile devices is referred to as crowdsensing. To enable the generation of two-dimensional (2D) as well as three-dimensional (3D) maps, we propose crowdsensing of point clouds, which are 3D data structures of points in space. For localization, we integrate two features of a recently developed mobile device, called Project Tango. Specifically, the Tango platform provides two main technologies for reliable localization, namely motion tracking and area learning. Moreover, Tango-powered devices provide us with the ability to collect point clouds though a third technology, called depth perception. In the past few years, spatial data obtained from range imaging was used to generate indoor maps. Nevertheless, range images are expensive and not always available. The required equipment, e.g. laser range scanners, are both expensive in procurement and require trained personnel for proper setup and operation. In this thesis, we aim for obtaining spatial point clouds via crowdsensing. The main idea is to use sensor data which can be scanned by volunteering individuals using easy to handle mobile devices. Specifically, we depend on depth perception capabilities as provided by Google Tango-powered tablet computers. A crowdsensing infrastructure assigns scanning tasks to individuals carrying a Tango device. Execution of such a task consists of taking scans of e.g. offices in a public building. The scanning results contain both spatial information about the room layout and its position. Energy consumption on the mobile device is reduced by applying Octree compression to the scanned point clouds, which results in a significant reduction of the amount of data, which has to be transferred to a back-end server. Afterwards, the back-end is responsible for assembling the received scans and the extraction of an indoors model. The modeling process - developed in this thesis - comprises two-phases. First, we extract a basic model from the obtained point clouds, which may contain outliers, inaccuracies and gaps. In the second phase, we refine the model by exploiting formal grammars. It is worth to mention here that we are the first to exploit formal grammars as a model fitting tool. We feed the information obtained in the first phase to an indoors grammar, which has been developed in the ComNSense project, University of Stuttgart. The resultant model both contains much less deviations from the ground truth and provides improved robustness against aberrations with respect to localization during the scanning process. Thus, instead of scanning multiple point clouds per room, we need only one scan to be able to construct an indoor map. During evaluation of this process, using scans of offices of our department, we were able to reproduce a model which is very close to the ground truth