
 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.

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

Location Based Service (LBS) is becoming increasingly valuable in many indoor application scenarios. However, existing indoor positioning technologies are either too costly or have high computational volumes. It is a challenge to achieve positioning both at low cost and low computation. To address this problem, we propose an indoor positioning algorithm based on computer vision, which can achieve localization at a low-cost and low computational complexity. In the proposed method, the lightweight MobileNet network is used to build an image classification model to train and optimize the collected dataset, then the predicted positions are obtained by inputting the images of the locations to be measured into the trained model. The experimental results demonstrate that the MobileNet model has lower computational effort than the classical convolutional neural network CNN. The designed computer vision-based method can successfully recognize images for indoor positioning, which is highly practical. ∗Corresponding author Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. APIT 2022, January 14–16, 2022, Virtual Event, Thailand © 2022 Association for Computing Machinery. ACM ISBN 978-1-4503-9557-1/22/01. . . $15.00 https://doi.org/10.1145/3512353.3512378 CCS CONCEPTS •Computingmethodologies→Artificial intelligence; Computer vision.

Yalan Su

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Low-cost and lightweight indoor positioning based on computer vision

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