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The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but...
We investigate the problem of graph matching to translate topological indoor localization to geographical localization, by modeling the building map and the semantic maps as graphs. A matching algorithm based on hidden Markov models is proposed. The matching algorithm is tested on both simulations and real data and accuracies as high as 94% on real data is achieved, while the matching is shown to...
In this paper, we propose an image-based localization system, applicable for a number of indoor scenarios including office buildings, airports, chain stores, etc. In such applications, text/numbers are suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read the text/numbers and provide a rough estimate using the floor plan. Next, it performs OCR-aided stereo...
In this paper, we propose a homography-aware semi-supervised formulation for the logo-based indoor localization problem using smartphone cameras. Our method labels unmatched feature points detected inside the logo parts of query images with their estimated 3D coordinates. The 3D coordinates are computed using the homography estimated from the matched features. We demonstrate the accuracy improvement...
We investigate the problem of graph matching to translate topological indoor localization to geographical localization, by modeling the building map and the semantic maps as graphs. A graph matching algorithm is proposed along with a node similarity measure based on finding the minimum distance between all sets of permutations of two vectors. We provide an efficient technique to calculate the similarity...
One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment load required to construct radio maps through fingerprinting. Several works aimed to employ radio propagation models as alternative to fingerprinting but the different sources of inaccuracies in the generation of these models result in high localization...
The sparse nature of location finding makes it desirable to exploit the theory of compressive sensing for indoor localization. In this paper, we propose a received signal strength (RSS)-based localization scheme in wireless local area networks (WLANs) using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an l1-minimization...
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