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Smartphone based personal tracking is very important for people to find their destination in large complex buildings (e.g. shopping malls, airports and museums). Such applications are highly demanded in both industries and research organizations. One critical issue for these applications is lack of mature technologies for highly accurate indoor location tracking. In this paper, a new Wi-Fi and magnetic...
The accuracy of a positioning system is usually expressed as its average position error in an experiment. However, when the ground truth is no longer available, it would still be useful to know the reliability of a position estimate based on a single measurement. To obtain a reliability metric, we hypothesize that there is a relation between the uncertainty in a position's posterior probability distribution,...
Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict...
High computational complexity hinders the widespread usage of neural networks, especially in mobile devices, which are often the basis of fine-grained localization technology for ubiquitous health monitoring, context awareness, and indoor location tracking. In this paper, we present a binarized recurrent neural network whose weight parameters, input, and intermediate hidden layer output signals, are...
This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it...
Selecting the appropriate parameters for an indoor positioning system may be a difficult task due to the large number of parameter combinations. It is more complex in realistic multi-building multi-floor environments, where severe wrong building and floor errors occur but they are not highlighted in the main evaluation metric. Moreover, a selected parameter configuration, that may seem appropriate...
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