Abstract
As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management.
Highlights
Nowadays, the determination of the location of many modern electronic devices is desirable, especially the one we are accustomed to using every day—a mobile phone
We have found the image-based positioning technique for positioning via mobile phone very efficient, and we decided to focus on the principles on which it occurs to calculate the position from a single image and the computer vision algorithms that are narrowly connected with this issue
We focused on two essential requirements—the use of a mobile phone camera and the automation of the whole process
Summary
The determination of the location of many modern electronic devices is desirable, especially the one we are accustomed to using every day—a mobile phone. One of the essential requirements for indoor positioning is higher accuracy in contrast to outdoor use. If the indoor positioning errors exceed several meters, the user cannot locate himself because his position can be in a wrong room or even on a wrong floor. Is higher user location accuracy essential for efficient indoor navigation, but the simplicity of its determination is essential as well. This is related to low acquisition costs, minimal maintenance, low maintenance costs, and minimal use of new onsite infrastructure
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