A low-cost indoor positioning system based on data-driven modeling for robotics research and education

Document Type

Article

Publication Date

9-11-2023

Abstract

This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

Identifier

85169041997 (Scopus)

Publication Title

Robotica

External Full Text Location

https://doi.org/10.1017/S0263574723000589

e-ISSN

14698668

ISSN

02635747

First Page

2648

Last Page

2667

Issue

9

Volume

41

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