AI 中文总结
研究基于惯性传感的姿态估计中IMU受额外加速度影响的问题，利用陀螺仪测量补偿额外加速度，引入无需外部设备的校准方法估计IMU参数，改进了依赖重力方向的姿态滤波器，在多种系统中证明了方法有效性。
AI 中文摘要
基于惯性传感的姿态估计需要通过陀螺仪和加速度计测量局部角速度和局部重力。然而，移动系统运动时，惯性测量单元（IMU）会受到额外加速度影响，使局部重力测量产生偏差，且离系统基座越远影响越大。许多姿态估计滤波器通过在高角速度时更多依赖陀螺仪积分来解决，但易积累长期误差。本文利用陀螺仪测量补偿系统运动引起的额外加速度，还引入校准方法，无需外部设备即可估计IMU固有参数及外部基座到IMU的向量。仿真和实际评估表明，该方法改进了依赖重力方向的姿态滤波器，还在高动态系统及IMU无法置于旋转中心的系统中证明了有效性。
英文摘要
Attitude estimation based on inertial sensing requires measurements of local angular velocities and local gravity via gyroscopes and accelerometers. However, during the motion of a mobile system the inertial measurement unit (IMU) will be subject to additional accelerations which skews the measurement of local gravity. This effect gets amplified the further away the IMU is from the base of the system. Many attitude estimation filters, such as "Madgwick" or "Mahony", account for this by relying more on gyroscope integration for periods of high angular velocity. However, this approach is prone to accumulate long term error especially around the gravity vector. In this work we utilize the gyroscope measurements to compensate the additional accelerations induced by the motion of the system, i.e., centripetal- and tangential-accelerations. Additionally, we introduce a calibration method that estimates intrinsic IMU parameters such as axes misalignment, bias, scale, as well as the extrinsic base-to-IMU vector without the necessity for additional external equipment. Our evaluation in simulation as well as in the real-world shows that this method improves any attitude filter that relies on the direction of gravity. Furthermore we demonstrate the effectivenes on highly dynamic systems, and systems that are unable to put the IMU at the center of rotation, using our real-world spherical mobile mapping system.
