Corridor Navigation for Monocular Vision Mobile Robots

Ng, Matthew

doi:10.15368/theses.2018.54

Cited by 1 publication

(1 citation statement)

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“…Furthermore, the GPS accuracy (namely, the distance error between the ground truth and the reported position) of low-cost sensors is at the level of ∼meters, and therefore, it cannot satisfy the requirements of many indoor applications. Existing indoor positioning methods can be classified into three categories, including pedestrian dead reckoning (PDR) [3][4][5], communication technology [6][7][8], and computer vision [9][10][11][12], and each has its advantages and drawbacks.…”

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confidence: 99%

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

Hong

Kyzer

et al. 2023

Robotica

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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.

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