People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to generate landmark-based route instructions to support people's wayfinding activities in unfamiliar indoor environments. Compared to other methods relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects, the proposed method relies on commonly available data about categories of spatial objects, which exist in most indoor spatial databases. With this, instructions like "Turn right after the second door, and use the elevator to go to the second floor" can be generated for indoor navigation. A case study with a university campus shows that the method is feasible in generating landmark-based route instructions for indoor navigation. More importantly, compared to metric-based instructions (i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users to unambiguously identify the correct decision point where a change of direction is needed, as well as offer information for the users to confirm that they are on the right way to the destination.

<p>Indoor navigation could be used in many applications to enhance performance in</p><p>its specific area. Anything from serious life critical tasks like aiding firefighters or</p><p>coordinating military attacks to more simple every day use like finding a desired</p><p>shop in a large supermarket could be considered. Smartphones of today introduce</p><p>an interesting platform with capabilities like existing, more clumsy, indoor</p><p>navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer</p><p>use its hardware in an efficient and easy way. The iPhone 3GS has a</p><p>3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering</p><p>which is used in this thesis to track the user on an indoor map. A particle</p><p>filter is used to track the position of the user. The implementation shows how</p><p>many particles the iPhone will be able to handle and update in real time without</p><p>lag in the application.</p>

Indoor navigation could be used in many applications to enhance performance in its specific area. Anything from serious life critical tasks like aiding firefighters or coordinating military attacks to more simple every day use like finding a desired shop in a large supermarket could be considered. Smartphones of today introduce an interesting platform with capabilities like existing, more clumsy, indoor navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer use its hardware in an efficient and easy way. The iPhone 3GS has a 3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering which is used in this thesis to track the user on an indoor map. A particle filter is used to track the position of the user. The implementation shows how many particles the iPhone will be able to handle and update in real time without lag in the application.

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / This paper describes the design and implementation of an autonomous robot to navigate indoors to a specified target using an inexpensive commercial off the shelf USB camera and processor running an imbedded Linux system. The robot identifies waypoints to aid in navigation, which in our case consists of a series of quick response (QR) codes. Using a 1080p USB camera, the robot could successfully identify waypoints at a distance of over 4 meters, and navigate at a rate of 50 cm/sec.

Do we always have to be lost in the halls of a big school on an already stressful first day of class? This paper has evaluated a prototype indoor navigation system that uses a ANT wireless protocol. The protocol has been placed into nodes (small electrical devices, hardware) which are then placed around an area of interest (a map), like beacons, using RSSI (signal from the nodes) to help determine where the subject is located. The mobile application is made specific to Android with a search algorithm that is based off of vector analysis with weighted percentages. The idea being that if the position of each node is available, knowing the location between the nodes should be easily achievable. The nodes were build successfully along with an android application to verify each nodes functionally and see the nodes RSSI values. The algorithm works as expected but due to several factors was not able to be fully realized. In the actual test the system results where slow and did not update in real time. It was found that the ANT protocol does not have a fast packet reception when using the continuous scan feature made available by ANT. The results of the search algorithm were not good enough for a real time indoor navigation prototype. The search algorithm was slow. The system needs more inputs to accurately locate a subject indoors.

Indoor navigation has always been an interesting topic to look at since there are many use of it for example in airports, huge malls and underground transport stations. One interesting application area is the underground transportation since the current navigation in the underground stations in Sweden is proven to be insufficient to guide the user from the underground station to the final destination. GPS is a prominent solution for the outdoor navigation but no prominent solution has been found for the indoor navigation. Techniques used for navigation indoors vary depending on the infrastructure of the building. Therefore, this paper looks at the different indoor navigation techniques categorized based on a smartphone's sensors and find the techniques most suitable in implementing for the existing SL underground stations. The underground station is divided into two parts, the platform and the second floor. A combination of kinematic and visual navigation techniques is efficient to implement in the platform due to the environment of the platform. Wireless navigation is suitable to implement in the second floor due to the availability of WiFi access points in the second floor of the underground station. Those findings help in setting up an efficient navigation for the SL underground stations, which help in navigating users from one point to another. / Inomhusnavigering har alltid varit ett intressant ämne att titta på, eftersom det finns stor användning av det till exempel i flygplatser, stora gallerior och tunnelbanestationer. Ett intressant tillämpningsomrade är den underjordiska transporten, eftersom den nuvarande navigeringen i tunnelbanestationerna i Sverige visar sig vara otillracklig for att navigera en resenär fran tunnelbanestationen till slutdestinationen. GPS ar en framträdande lösning för utomhusnavigeringen men ingen framstående losning har hittats för inomhusnavigeringen. Tekniker som används för navigering inomhus varierar beroende på byggnadens infrastruktur. Därfor studerar denna rapport på de olika inomhusnavigeringsteknikerna som kategoriseras baserat på en smartphones sensorer och hittar de tekniker som är mest passande för att genomföra för de befintliga SL-tunnelbanestationerna. Tunnelbanestationen är uppdelad i två delar, plattformen och andra våningen. En kombination av kinematisk och visuell navigeringsteknik är effektiv att implementera i plattformen på grund av plattformens miljö. Trådlös navigering är lamplig att genomföra i andra våningen på grund av tillgangen till WiFi-åtkomstpunkter i andra våningen av tunnelbanestationen. Det resultatet hjälper till att skapa en effektiv navigering för SL-tunnelbanestationerna, vilket hjälper till att navigera användare från en punkt till en annan.

Finding ones way in unknown and unfamiliar environments is a common task. A number of tools ranging from paper maps to location-based services have been introduced to assist human navigation. Undoubtedly, car navigation systems can be considered the most successful example of location based services that widely gained user acceptance. However the concept of car navigation is not always (perhaps rarely) suitable for pedestrian navigation. Moreover, precise localization of moving objects indoors is not possible due to the absence of an absolute positioning method such as GPS. These make accurate indoor tracking and navigation an interesting problem to explore. Many of the methods of spatial analysis popular in outdoor applications can be used indoors. In particular, generation of the indoor navigation network can be an effective solution for a) improving the navigation experience inside complex indoor structures and b) enhancing the analysis of the indoor tracking data collected with existing positioning solutions. Such building models should be based on a graph representation and consist of the number of ‘nodes’ and ‘edges’, where ‘nodes’ correspond to the central position of the room and ‘edge’ represents the medial axis of the hallway polygons, which physically connects these rooms. Similar node-links should be applied stairs and elevators to connect building floors. To generate this model, I selected the campus of University of Saskatchewan as the study area and presented a method that creates an indoor navigation network using ESRI ArcGIS products. First, the proposed method automatically extracts geometry and topology of campus buildings and computes the distances among all entities to calculate the shortest path between them. The system navigates through the University campus and it helps locating classrooms, offices, or facilities. The calculation of the route is based on the Dijkstra algorithm, but could employ any network navigation algorithm. To show the advantage of the generated network, I present results of a study conducted in conjunction with the department of Computer Science. An experiment that included 37 participants was designed to collect the tracking data on a university campus to demonstrate how the incorporation of the indoor navigation model can improve the analysis of the indoor movement data. Based on the results of the study, it can be concluded that the generated indoor network can be applied to raw positioning data in order to improve accuracy, as well as be employed as a stand-alone tool for enhancing of the route guidance on a university campus, and by extension any large indoor space consisting of individual or multiple buildings.

Libraries are filled with indistinct landmarks, such as rows of identical bookshelves, which make it difficult for patrons to navigate and locate books. Call numbers are the primary resource that can be utilized while locating books, and these consist of letters and digits that are incomprehensible to many library users. With libraries increasingly offering both physical and digital resources and mobile devices being more common, we aimed to investigate the potential for mobile devices to assist in locating books within the complex physical spaces of libraries.

A novel approach is proposed for real-time retrieval of images from a large database of overlapping images of an indoor environment. The procedure extracts visual features from images using selected computer vision techniques, and processes the extracted features to create a reduced list of features annotated with the frame numbers they appear in. This method is named landmark indexing. Unlike some state-of-the-art approaches, the proposed method does not need to consider large image adjacency graphs because the overlap of the images in the map sufficiently increases information gain, and mapping of similar features to the same landmark reduces the search space to improve search efficiency. Empirical evidence from experiments on real datasets shows high (90-100%) accuracy in image retrieval, and improvement in search time from the order of 100-200 milliseconds to the order of 10-30 milliseconds. The image retrieval technique is also demonstrated by integrating it into a 3D real-time navigation system. This system is tested in several indoor environments and all experiments show accurate localization results in large indoor areas with errors in the order of 15-20 centimeters only. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2014-04-24 12:44:41.429

In our day to day activity, imagine if you go to a museum, hospital or any kind of huge building. You need to find the best way to get into a specific depart- ment. It might be difficult to find the way even if you have the map of the building in your hand. Indoor positioning systems can be used to locate people or objects inside a building, using radio waves, signals, or other sensory information collected by a smartphone or tablet. Bluetooth Low Energy (BLE) beacons broadcast signals, and bluetooth devices, such as smartphones, can then receive these signals. BLE devices can take Received Signal Strength Indication (RSSI) information together with an algorithm to calculate the location of the user. This is a useful method for indoor environments when using Global Positioning System (GPS) is not an option [1]. In this project I tried to find a better solution for localization and navigation when GPS does not work. The focus of the project is to use communication be- tween smartphones and beacons, for guidance in inside environments, without using GPS. This thesis is about the applications I produced, which can be used for indoor localization and navigation. Using the applications, you can map any building such as university, hospital, museum, big mall etc. To map a building, you upload a map of the building and put waypoints where you placed beacons. Once mapping is done, you can log in to the web admin and put some informa- tion for each beacon. As a user, when outside a nearby mapped building, your phone can get notified (trough bluetooth), and you can download the user app, which includes the map of the building and shows your location. With the user app you can easily find your favorite places in the building and get information about place near you.