Position awareness in unknown and large indoor spaces represents a great advantage for people, everyday pedestrians have to search for specific places, products and services. In this work a positioning solution able to localize the user based on data measured with a mobile device is described and evaluated. The position estimate uses data from smartphone built-in sensors, WiFi (Wireless Fidelity) adapter and map information of the indoor environment (e.g. walls and obstacles). A probability map derived from statistical information of the users tracked location over a period of time in the test scenario is generated and embedded in a map graph, in order to correct and combine the position estimates under a Bayesian representation. PDR (Pedestrian Dead Reckoning), beacon-based Weighted Centroid position estimates, map information obtained from building OpenStreetMap XML representation and probability map users path density are combined using a Particle Filter and implemented in a smartphone application. Based on evaluations, this work verifies that the use of smartphone hardware components, map data and its semantic information represented in the form of a OpenStreetMap structure provide 2.48 meters average error after 1,700 travelled meters and a scalable indoor positioning solution. The Particle Filter algorithm used to combine various sources of information, its radio WiFi-based observation, probability particle weighting process and the mapping approach allowing the inclusion of new indoor environments knowledge show a promising approach for an extensible indoor navigation system.
Robust Indoor Localization Using Optimal Fusion Filter For Sensors And Map Layout Information
(2013)
A person has to deal with large and unknown scenarios, for example a client searching for a expositor in a trade fair or a passenger looking for a gate in an airport. Due to the fact that position awareness represents a great advantage for people, a navigation system implemented for a commercial smartphone can help the user to save time and money.
In this work a navigation example application able to localize and provide directions to a desired destination in an indoor environment is presented and evaluated. The position of the user is calculated with information from the smartphone built-in sensors, WiFi adapter and floor-plan layout of the indoor environment.
A commercial smartphone is used as the platform to implement the example application, due to it's hardware features, computational power and the graphic user interface available for the users. Evaluations verified that room accuracy is achieved for robust localization by using the proposed technologies and algorithms. The used optimal sensor fusion filter for different sources of information and the easy to deploy infrastructure in a new environment show promise for mobile indoor navigation systems.
Advances in computer graphics enable us to create digital images of astonishing complexity and realism. However, processing resources are still a limiting factor. Hence, many costly but desirable aspects of realism are often not accounted for, including global illumination, accurate depth of field and motion blur, spectral effects, etc. especially in real-time rendering. At the same time, there is a strong trend towards more pixels per display due to larger displays, higher pixel densities or larger fields of view. Further observable trends in current display technology include more bits per pixel (high dynamic range, wider color gamut/fidelity), increasing refresh rates (better motion depiction), and an increasing number of displayed views per pixel (stereo, multi-view, all the way to holographic or lightfield displays). These developments cause significant unsolved technical challenges due to aspects such as limited compute power and bandwidth. Fortunately, the human visual system has certain limitations, which mean that providing the highest possible visual quality is not always necessary. In this report, we present the key research and models that exploit the limitations of perception to tackle visual quality and workload alike. Moreover, we present the open problems and promising future research targeting the question of how we can minimize the effort to compute and display only the necessary pixels while still offering a user full visual experience.
