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.

移动机器人的定位，地图绘制和导航系统通常采用惯性测量单元来获取机器人的加速度和角速度。但是，由于校准不良而导致的惯性测量单元的内部和外部参数错误会直接影响机器人定位和姿势估计的准确性。尽管可通过惯性测量单元使用的成熟的内部参数校准方法解决了此问题，但缺少惯性测量单元与移动机器人底盘之间的外部参考校准方法。本研究通过提出一种基于主成分分析和非线性优化的麦克纳姆轮全向移动平台内部参数校准和麦克纳姆底盘-惯性测量单元外部参数校准的新算法，该算法专为装备有摄像头，惯性的机器人而设计测量单元，麦克纳姆轮和轮速里程表，并在对惯性测量单元的内部参数以及摄像机的内部和外部参数进行精确校准的前提下起作用。所有内部和外部参数校准均使用机器人的现有设备进行，而无需其他校准工具。该方法在麦克纳姆轮全向移动平台上的应用验证了该方法的可行性。从而证明了所提出的校准方法可以保证机器人姿态估计的准确性。