A Novel Visual Indoor Positioning Method With Efficient Image Deblurring

Abstract

Aiming to improve the accuracy of visual indoor positioning, an efficient deep multi-patch network based image deblurring algorithm (DMPID) is proposed to eliminate the effect of blurred images on the positioning accuracy. Meanwhile, the self-sorting visual word based image retrieval algorithm and the block based improved eight-point method are also proposed in this paper to improve the retrieval accuracy and positioning accuracy. The proposed image deblurring algorithm adopts the deep multi-patch network and the weight selective sharing scheme to acquire the deblurred images. Then, we propose the self-sorting visual word and add two additional elements representing the relative spatial information into every obtained feature point to utilize the intrinsic relationships between extracted features and physical positions in order to improve the retrieval accuracy. Meanwhile, the visual word filtering is also employed to eliminate redundant visual words and reduce the time consumption of image retrieval. Finally, the position estimation of query camera can be achieved by the epipolar constraint based on the improved eight-point method. Simulation results and performance analysis show that the proposed method can restore the image details effectively and improve the accuracy of visual indoor positioning.