神经网络与传统滤波器在基于惯性传感器的姿态估计中的比较
Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation
摘要：
惯性测量单元通常用于估计运动物体的姿态。许多非线性滤波方法已经被提出来解决固有的传感器融合问题。然而，当考虑大范围的不同的动态和静态旋转和平移运动时，可达到的精度受限于对加速度计和陀螺仪融合权重的情况相关调整的需要。我们调查到何种程度上可以克服这些限制，通过人工神经网络和有多少特定领域的优化的神经网络模型需要优于传统的过滤器解决方案。一组不同的运动记录与标记为基础的光学地面实况用于性能评估和比较。建议的神经网络被发现优于传统的过滤器在所有的运动，只有当特定领域的优化被引入。我们的结论是，他们是一个很有前途的工具，基于惯性传感器的实时姿态估计，但需要专家知识和丰富的数据集，以实现最佳性能。
英文摘要：
Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network model is required to outperform the conventional filter solution. A diverse set of motion recordings with a marker-based optical ground truth is used for performance evaluation and comparison. The proposed neural networks are found to outperform the conventional filter across all motions only if domain-specific optimizations are introduced. We conclude that they are a promising tool for inertial-sensor-based real-time attitude estimation, but both expert knowledge and rich datasets are required to achieve top performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
