Abstract
This paper describes an online method to calibrate certain kinematic parameters of legged robots, including leg lengths, that can be difficult to measure offline due to dynamic deformation effects and rolling contacts. A kinematic model of the robot’s legs that depends on these parameters is used, along with measurements from joint encoders, foot contact sensors, and aninertial measurement unit (IMU) to predict the robot’s body velocity. This predicted velocity is then compared to another velocity measurement from, for example, a camera or motion capture system, and the difference between them is used to compute anupdate on the kinematic parameters. The method can be incorporated into both Kalman filter or sliding-window optimization-based state estimator. We provide a theoretical observability analysis of our method, as well as validation both in simulation and on hardware. Hardware experiments demonstrate that online kinematic calibration can significantly reduce position drift when relying on odometry.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
