Indoor Localization with a Signal Tree
Indoor localization based on image matching faces the challenges of clustering large amounts of images to build a reference database, costly query when the database is large and indistinctive image features in buildings with unified decoration style. We propose a novel indoor localization algorithm using smartphones where WiFi, orientation and visual signals are fused together to improve the localization performance. The reference database is built as a signal tree with less computational cost as WiFi and orientation signals pre-cluster the reference images. During localization, WiFi and orientation signals not only offer more context information, but also prune impossible reference images, improving the accuracy and efficiency of image matching. In addition, images are described by multiple-level descriptors recording both global and local image information. The proposed method is compared with other methods in terms of localization accuracy, localization efficiency and time cost to build the reference database. Experimental results on four large university buildings show that our algorithm is efficient and accurate for indoor localization.
W. Jiang and Z. Yin, "Indoor Localization with a Signal Tree," Proceedings of the 2015 18th International Conference on Information Fusion, Fusion 2015 (2015, Washington, DC), pp. 1724-1731, Institute of Electrical and Electronics Engineers (IEEE), Sep 2015.
18th International Conference on Information Fusion (2015: Jul. 6-9, Washington, DC)
Keywords and Phrases
College Buildings; Database Systems; Efficiency; Image Matching; Image Recording; Information Fusion; Query Processing; Trees (Mathematics); Computational Costs; Context Information; Image Information; Indoor Localization; Localization Accuracy; Localization Performance; Multiple Levels; Reference Database; Indoor Positioning Systems
International Standard Book Number (ISBN)
Article - Conference proceedings
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