View source for 18338389. Method Of Calibrating Camera To Robot, System, And Non-Transitory Computer-Readable Storage Medium Storing Computer Program simplified abstract (SEIKO EPSON CORPORATION)

=Method Of Calibrating Camera To Robot, System, And Non-Transitory Computer-Readable Storage Medium Storing Computer Program=


==Organization Name==

[[:Category:SEIKO EPSON CORPORATION|SEIKO EPSON CORPORATION]]


[[Category:SEIKO EPSON CORPORATION]]

==Inventor(s)==

[[:Category:Akinobu Sato of Matsumoto (JP)|Akinobu Sato of Matsumoto (JP)]][[Category:Akinobu Sato of Matsumoto (JP)]]

==Method Of Calibrating Camera To Robot, System, And Non-Transitory Computer-Readable Storage Medium Storing Computer Program - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18338389 titled 'Method Of Calibrating Camera To Robot, System, And Non-Transitory Computer-Readable Storage Medium Storing Computer Program

==Simplified Explanation==

The present disclosure describes a method for calibrating a robot arm using machine learning models and image data from a camera. The method involves estimating pixel coordinate values of characteristic points on the robot arm from an image captured by the camera using a first machine learning model. These pixel coordinate values are then used to estimate the first coordinate values of the characteristic points in a 3D camera coordinate system using a second machine learning model. The second coordinate values of the characteristic points in a 3D robot coordinate system are calculated using the encoder value of the robot arm. Finally, this process is repeated for multiple postures of the robot arm to estimate calibration parameters, including the external parameter of the camera, using the first and second coordinate values.

* Estimation of pixel coordinate values of characteristic points on a robot arm from camera images using a machine learning model.
* Estimation of first coordinate values of the characteristic points in a 3D camera coordinate system using the pixel coordinate values and another machine learning model.
* Calculation of second coordinate values of the characteristic points in a 3D robot coordinate system using the encoder value of the robot arm.
* Iterative process for estimating calibration parameters, including the external parameter of the camera, using the first and second coordinate values of the characteristic points in multiple robot arm postures.

== Potential Applications ==
* Robotic arm calibration for precise positioning and control.
* Industrial automation and manufacturing processes.
* Robotics research and development.

== Problems Solved ==
* Accurate calibration of a robot arm using image data and machine learning models.
* Simplification of the calibration process by estimating coordinate values in different coordinate systems.
* Efficient estimation of calibration parameters using multiple robot arm postures.

== Benefits ==
* Improved accuracy and precision in robot arm positioning and control.
* Time and cost savings in the calibration process.
* Flexibility in adapting to different robot arm postures and configurations.


==Original Abstract Submitted==

A method according to the present disclosure includes (a) estimating pixel coordinate values of a plurality of characteristic points set in advance in a robot arm from an image of the robot arm taken by a camera using a first machine learning model, (b) estimating first coordinate values of the plurality of characteristic points in a 3D camera coordinate system from the pixel coordinate values of the plurality of characteristic points using a second machine learning model, (c) calculating second coordinate values of the plurality of characteristic points in a 3D robot coordinate system using an encoder value of the robot arm, and (d) executing the steps (a) through (c) with respect to a plurality of postures of the robot arm to estimate calibration parameters including an external parameter of the camera using the first coordinate values and the second coordinate values of the plurality of characteristic points in the plurality of postures.



[[Category:G06T7/80]]