Design of a mobile augmented reality-based indoor navigation system

GPS-based navigation technology has been widely used in most of the commercial navigation applications nowadays. However, its usage in indoor navigation is not as effective as when it is used at outdoor environment. Much research and developments of indoor navigation technology involve additional hardware installation which usually incur high setup cost. In this paper, research and comparisons were done to determine the appropriate techniques of indoor positioning, pathfinding, and route guidance for an indoor navigation method. The aim of this project is to present a simple and cost effective indoor navigation system. The proposed system uses the existing built-in sensors embedded in most of the mobile devices to detect the user location, integrates with AR technology to provide user an immersive navigation experience. In this project, an indoor navigation mobile application was developed and tested. The development demonstrates the usage of Indoor Atlas which enables indoor positioning through technology fusion to detect user’s position and obtain the route to destination, and AR Core to display AR guidance using the computed route. Surveys were carried out to gauge the efficiency of the method and to gather the feedback from the participants. The architecture of the method and the demonstration of the application is presented

Revisión de la literatura de modelos matemáticos para el tránsito de personas con discapacidad visual a través de rutas accesibles y seguras

Esta investigación parte del análisis de los problemas de accesibilidad que tienen las personas con discapacidad para transitar a través de la ciudad. En particular, para las personas con discapacidad visual transitar, navegar y orientarse resulta un reto particularmente difícil porque sus sentidos no perciben la misma información que un peatón sin discapacidad visual. En el Perú un 61.7% de las personas con discapacidad visual no utiliza ningún elemento de apoyo para movilizarse, además presentan dificultades para adquirirlos por su elevado costo. El principal elemento de apoyo para el tránsito de personas con discapacidad visual es el bastón blanco; sin embargo, ese objeto tiene un rango limitado de acción. Por esta razón se han desarrollado soluciones que permitan que dichas personas transiten de manera segura a través de la ciudad. Entre estas soluciones se encuentran los dispositivos electrónicos que sirven para detectar obstáculos, los dispositivos para navegación mediante radio señales, algoritmos de optimización para encontrar rutas seguras y accesibles y los sistemas de apoyo para navegación que incluye tecnología de inteligencia artificial, Big Data e Internet of Things (IoT). La revisión de la literatura de este trabajo de investigación revela que la solución que utiliza el IoT es la que cubre la mayoría de aspectos de la problemática de tránsito para personas invidentes (detección de obstáculos, navegación y accesibilidad). Asimismo, que en dicha solución también se utilizan algoritmos de optimización que permiten a los usuarios acceder a distintos destinos y que han presentado indicadores de rendimiento que sugieren que estas herramientas de optimización benefician a las personas con discapacidad visual en su movilidad e independencia. Por último, se debe considerar que estas herramientas de optimización deben utilizarse considerando las limitaciones o preferencias de las personas con discapacidad visual, no solo consiste en encontrar la ruta más corta, sino en la medida de los posible encontrar la ruta más segura y accesible.Trabajo de investigació

The last-seen image : an image-based approach for finding lost objects using a head-mounted display

Considering the current development of commercial head-mounted displays (HMD), it is likely that HMDs will be widely used in the near future. Therefore, it becomes feasible to build systems that rely on HMD technology. Real-world search engines aim at supporting the user's search capabilities in the real world. HMDs are a possible device to guide a user towards the location of an object. To ensure a high usability, it is essential to find suitable location representations of search results on an HMD. Based on previous findings, we present a novel location representation called "last-seen image" to locate objects in a known environment (e.g. the user’s home or office building). The last-seen image shows the picture of a sought object including the surrounding context of the object. We implemented a prototype on an HMD using WiFi indoor positioning to provide the proposed visualization as well as a map visualization. We conducted a user study comparing our proposed last-seen image approach to a map based approach. The last-seen image showed to be significantly faster for finding harder hidden objects compared to the map representation. However, the map was favored for finding the correct room. Therefore, we propose a hybrid system using the map representation to find the correct room and using the last-seen image to find the object on room-level.Aufgrund der aktuellen Entwicklung von Head-Mounted Displays (HMD) ist es sehr wahrscheinlich, dass HMDs in Zukunft allgegenwärtig sein werden. Deshalb wird ein System, welches auf der Benutzung von HMDs basiert, realisierbar. Real-World Search Engines unterstützen einen User, verlorene Gegenstände wiederzufinden. HMDs eignen sich zur Repräsentation solcher Suchergebnisse. Um die Benutzbarkeit solcher Systeme zu garantieren, ist es wichtig, eine passende Repräsentationsart zu finden. Aufgrund vorheriger Ergebnisse stellen wir eine neuartige Repräsentationsart zur Objektlokalisierung vor: das Last-Seen Image. Das Last-Seen Image zeigt das Bild eines gesuchten Gegenstandes, welches nicht nur den Gegenstand selbst, sondern auch die Umgebung zeigt. Wir haben einen Prototypen entwickelt, welcher auf einem HMD eine Kartenansicht und das Last-Seen Image bereitstellt. Daraufhin haben wir eine Benutzerstudie durchgeführt, um das Last-Seen Image mit der Kartendarstellung zu vergleichen. Es hat sich gezeigt, dass schwer versteckte Objekte mithilfe des Last-Seen Image deutlich schneller gefunden werden als mit der Kartendarstellung. Jedoch wurde die Karte bevorzugt, um den richtigen Raum zu finden. Deshalb empfehlen wir die Benutzung eines Hybriden Systems, welches die Kartendarstellung verwendet, um den richtigen Raum zu finden. Sobald man sich in dem richtigen Raum befindet wird das Last-Seen Image angezeigt

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Comparision of pathfinding algorithms for visually impaired people in IoT based smart buildings

Indoor navigation is highly challenging for visually impaired, particularly when visiting an unknown environment with complex design. In addition, a person at the entrance of the building might not be aware of distant changes/disruption in the path to the destination. Internet of Things devices can become the foundation infrastructure for scanning the dynamic changes in such an environment. With the sensory data of the scanned nodes, a dynamic pathfinding algorithm can provide guided route considering the changes to the destination. There are various pathfinding algorithms proposed for indoor environment including A*, Dijkstra’s, probabilistic roadmap, recursive tree and orthogonal jump point search. However, there is no study done to find if these algorithms are suited to the special requirements of low vision people. We have carried out simulations in MATLAB to evaluate the performance of these algorithms based on parameters such as distance and nodes travelled execution time and safety. The results provide strong conclusion to implement most suitable orthogonal jump point search to achieve optimal and safe path for low vision people in complex buildings