Reduced Calibration Strategy Using a Basketball for RGB-D Cameras

Luis-Rogelio Roman-Rivera, Israel Sotelo-Rodríguez, Jesus Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguín and Efrén Gorrostieta-Hurtado
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Luis-Rogelio Roman-Rivera: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico
Israel Sotelo-Rodríguez: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico
Jesus Carlos Pedraza-Ortega: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico
Marco Antonio Aceves-Fernandez: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico
Juan Manuel Ramos-Arreguín: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico
Efrén Gorrostieta-Hurtado: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro C.P. 76010, Mexico

Mathematics, 2022, vol. 10, issue 12, 1-15

Abstract: RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers.

Keywords: RGB-D camera; RGB-D camera calibration; spherical object; 3D reconstruction; sphere detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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