Abstract
This paper proposes a method to calibrate the model used for inverse perspective mapping of humanoid robots. It aims at providing a reliable way to determine the robot’s position given the known objects around it. The position of the objects can be calculated using coordinate transforms applied to the data from the robot’s vision device. Those transforms are dependent on the robot’s joint angles (such as knee, hip) and the length of some components (e.g. torso, thighs, calves). In practice, because of the sensitivity of the transforms with respect to the inaccuracies of the mechanical data, this calculation may yield errors that make it inadequate for the purpose of determining the objects’ positions. The proposed method reduces those errors using an optimization algorithm that can find offsets that can compensate those mechanical inaccuracies. Using this method, a kid-sized humanoid robot was able to determine the position of objects up to 2 m away from the itself with an average of 3.4 cm of error.
Supported by CNPQ (Conselho Nacional de Desenvolvimento Científico e Tecnológico).
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Acknowledgment
Francisco da Silva thanks CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for his undergraduate research scholarship. Marcos Maximo is partially funded by CNPq through the grant 307525/2022-8. Takashi Yoneyama is partially funded by CNPq through the grant 304134/2-18-0. We thank the entire ITAndroids team, especially Lucas Steuernagel, for helping during data collection tests. We thank Robocup’s sponsors MathWorks, FESTO, SoftBank Robotics and United Robotics Group for making all this development in robotics possible.
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da Silva, F.B.D.R., de Albuquerque Máximo, M.R.O., Yoneyama, T., Barroso, D.H.V., Aki, R.T. (2024). Calibration of Inverse Perspective Mapping for a Humanoid Robot. In: Buche, C., Rossi, A., Simões, M., Visser, U. (eds) RoboCup 2023: Robot World Cup XXVI. RoboCup 2023. Lecture Notes in Computer Science(), vol 14140. Springer, Cham. https://doi.org/10.1007/978-3-031-55015-7_10
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