Next Article in Journal
Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data
Next Article in Special Issue
3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL
Previous Article in Journal
Meet the Virtual Jeju Dol Harubang—The Mixed VR/AR Application for Cultural Immersion in Korea’s Main Heritage
Previous Article in Special Issue
Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models
Open AccessArticle

Indoor Positioning Using PnP Problem on Mobile Phone Images

1
Plan4all, 33012 Horní Bříza, Czech Republic
2
Department of Geomatics, University of West Bohemia, 30100 Plzeň, Czech Republic
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 368; https://doi.org/10.3390/ijgi9060368
Received: 15 May 2020 / Accepted: 30 May 2020 / Published: 2 June 2020
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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. View Full-Text
Keywords: indoor positioning system; image-based positioning system; computer vision; SIFT; feature detection; feature description; cell phone camera; PnP problem; projection matrix; epipolar geometry; OpenCV indoor positioning system; image-based positioning system; computer vision; SIFT; feature detection; feature description; cell phone camera; PnP problem; projection matrix; epipolar geometry; OpenCV
Show Figures

Figure 1

MDPI and ACS Style

Kubíčková, H.; Jedlička, K.; Fiala, R.; Beran, D. Indoor Positioning Using PnP Problem on Mobile Phone Images. ISPRS Int. J. Geo-Inf. 2020, 9, 368.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop