Image-based indoor localization system based on 3D SfM model

Guoyu Lu; Chandra Kambhamettu

doi:10.1117/12.2038582

3 February 2014 Image-based indoor localization system based on 3D SfM model

Guoyu Lu, Chandra Kambhamettu

Proceedings Volume 9025, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques; 90250H (2014) https://doi.org/10.1117/12.2038582
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

Indoor localization is an important research topic for both of the robot and signal processing communities. In recent years, image-based localization is also employed in indoor environment for the easy availability of the necessary equipment. After capturing an image and sending it to an image database, the best matching image is returned with the navigation information. By allowing further camera pose estimation, the image-based localization system with the use of Structure-from-Motion reconstruction model can achieve higher accuracy than the methods of searching through a 2D image database. However, this emerging technique is still only on the use of outdoor environment. In this paper, we introduce the 3D SfM model based image-based localization system into the indoor localization task. We capture images of the indoor environment and reconstruct the 3D model. On the localization task, we simply use the images captured by a mobile to match the 3D reconstructed model to localize the image. In this process, we use the visual words and the approximate nearest neighbor methods to accelerate the process of nding the query feature's correspondences. Within the visual words, we conduct linear search in detecting the correspondences. From the experiments, we nd that the image-based localization method based on 3D SfM model gives good localization result based on both accuracy and speed.

Citation Download Citation

Guoyu Lu and Chandra Kambhamettu "Image-based indoor localization system based on 3D SfM model", Proc. SPIE 9025, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, 90250H (3 February 2014); https://doi.org/10.1117/12.2038582

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