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Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization
arXiv - CS - Robotics Pub Date : 2020-05-17 , DOI: arxiv-2005.08284
Gang Peng, Zezao Lu, Zejie Tan, Dingxin He, Xinde Li

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal reference calibration methods available for IMUs, external reference calibration methods between the IMU and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel chassis-IMU internal and external parameter calibration algorithm based on nonlinear optimization, which is designed for robots equipped with cameras, IMUs, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the IMU and the internal and external parameters of the camera. All of the internal and external reference 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 as an example, as well as suitable for other type chassis of mobile robots. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.

中文翻译:

基于非线性优化的轮式机器人移动底盘及惯性测量单元内外参数标定

移动机器人定位、测绘和导航系统通常采用惯性测量单元(IMU)来获取机器人的加速度和角速度。然而,由于标定不良引起的IMU内部和外部参数的误差直接影响机器人定位和位姿估计的准确性。虽然该问题已通过 IMU 可用的成熟内部参考校准方法得到解决,但缺乏 IMU 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非线性优化的新型底盘 IMU 内外参数校准算法来解决这个问题,该算法专为配备摄像头、IMU 和轮速里程表的机器人而设计,并在对IMU内部参数和相机内外参数进行准确标定的前提下发挥作用。所有内部和外部参考校准均使用机器人的现有设备进行,无需额外的校准辅助工具。以麦克纳姆轮全向移动平台为例,验证了该方法的可行性,也适用于其他类型的移动机器人底盘。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。以麦克纳姆轮全向移动平台为例,验证了该方法的可行性,也适用于其他类型的移动机器人底盘。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。以麦克纳姆轮全向移动平台为例,验证了该方法的可行性,也适用于其他类型的移动机器人底盘。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。
更新日期:2020-05-19
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