A novel algorithm based on nonlinear optimization for parameters calibration of wheeled robot mobile chasses

摘要

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.