This work presents a centralized multi-IMU filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel EKF-based method estimates the positional and rotational offsets of the system of sensors as well as their intrinsic biases without the use of rigid body geometric constraints. Additionally, the filter is flexible in the total number of sensors used while leveraging the commonly used MSCKF framework for camera measurements. The filter framework has been validated using Monte Carlo simulation as well as experimentally. In both simulations and experiments, using multiple IMU measurement streams within the proposed filter framework outperforms the use of a single IMU in a filter prediction step while also producing consistent and accurate estimates of initial calibration errors. Compared to current state-of-the-art optimizers, the filter produces similar intrinsic and extrinsic calibration parameters for each sensor. Finally, an open source repository has been provided at https://github.com/unmannedlab/ekf-cal containing both the online estimator and the simulation used for testing and evaluation.

翻译：本文提出了一种集中式多IMU滤波框架，可实现未同步惯性测量单元的在线内参和外参标定，且对标定参数变化具有鲁棒性。该基于扩展卡尔曼滤波（EKF）的新方法无需依赖刚体几何约束，即可估计传感器系统的位置偏移、姿态偏移及其内参偏差。此外，该滤波器在灵活适配所使用的传感器总数量的同时，采用广泛应用于MSCKF框架的相机测量方法。该滤波框架已通过蒙特卡洛仿真和实验验证。在仿真与实验中，采用多IMU测量流联合的滤波框架，在滤波预测阶段的表现优于单IMU方案，同时能生成一致且准确的初始标定误差估计。与现有最优化方法相比，该滤波器为每个传感器输出的内外标定参数精度相当。最后，开源代码库已发布于https://github.com/unmannedlab/ekf-cal，包含在线估计器及用于测试和评估的仿真代码。