A Weighted KNN Epipolar Geometry-Based Approach for Vision-Based Indoor Localization Using Smartphone Cameras

We propose a weighted KNN Epipolar Geometry-based method for vision-based indoor localization using cellphone cameras. The proposed method is applicable for fine localization whenever a pose-tagged (position + rotation matrix) image database is available rather than just Geo-tagged one. To the best of our knowledge, this is the first that Epipolar geometry has been utilized for fine localization in indoor applications using smartphone images. We compare the performance of our method with two outstanding literature works. It will be also demonstrated that the proposed method can extrapolate the location of queries located outside of the database location set, as well as compensate for the small databases, where database location set is sparse as two additional new features of this method.