Abstract
Indoor positioning is getting more and more attention and research. We propose an epipolar geometry-based method for vision-based indoor localization using images. It needs an image collected in the positon that is aiming to localize. It uses SURF to pick up the feature points and filtrate them to remain good ones and get rid of bad ones. The good feature points are used to match the feature points in the database. (The feature points are selected by the images whose positions are already known). We use the matched feature points to calculate the essential matrix that include the translation information and rotary information. Then we can complete the localization by the relationship between the query image and the images in the database. What’s more we use the feature points to replace the images to build the database aiming to reduce the space and speed up the localization.
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Liu, Y., Ma, L., Wang, X., Meng, W. (2019). An Epipolar Geometry-Based Approach for Vision-Based Indoor Localization. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_320
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DOI: https://doi.org/10.1007/978-981-10-6571-2_320
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