论文标题
最佳轴组成扩展：多个陀螺仪和加速度计数据融合以减少系统错误
Best Axes Composition Extended: Multiple Gyroscopes and Accelerometers Data Fusion to Reduce Systematic Error
论文作者
论文摘要
与单个IMU相比，多个刚性连接的惯性测量单元（IMU）传感器提供了更丰富的数据流。最先进的方法遵循IMU测量的概率模型，该模型基于贝叶斯框架下的错误的随机性质。但是，负担得起的低级IMU此外，由于其不受相应的概率模型所掩盖的缺陷，因此遭受了系统的错误。在本文中，我们提出了一种方法，即结合多个IMU（MIMU）传感器数据的最佳轴组成（BAC），以通过从所有可用轴集中动态选择最佳的IMU轴来考虑到随机和系统错误的准确估计。我们在MIMU视觉惯性传感器上评估了我们的方法，并将方法的性能与MIMU数据融合的最新方法进行比较。我们表明，BAC的表现优于后者，并且在开放环路中的方向和位置估计都可以提高20％的精度，但需要适当的处理以保持获得的增益。
Multiple rigidly attached Inertial Measurement Unit (IMU) sensors provide a richer flow of data compared to a single IMU. State-of-the-art methods follow a probabilistic model of IMU measurements based on the random nature of errors combined under a Bayesian framework. However, affordable low-grade IMUs, in addition, suffer from systematic errors due to their imperfections not covered by their corresponding probabilistic model. In this paper, we propose a method, the Best Axes Composition (BAC) of combining Multiple IMU (MIMU) sensors data for accurate 3D-pose estimation that takes into account both random and systematic errors by dynamically choosing the best IMU axes from the set of all available axes. We evaluate our approach on our MIMU visual-inertial sensor and compare the performance of the method with a purely probabilistic state-of-the-art approach of MIMU data fusion. We show that BAC outperforms the latter and achieves up to 20% accuracy improvement for both orientation and position estimation in open loop, but needs proper treatment to keep the obtained gain.
