论文标题
基于惯性传播的态度估计的神经网络与常规过滤器
Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation
论文作者
论文摘要
惯性测量单元通常用于估计移动物体的态度。已经提出了许多非线性滤波器方法来解决固有的传感器融合问题。但是，当考虑了大量不同的动态和静态旋转和翻译运动时，可实现的准确性受到依赖于加速度计和陀螺仪融合权重的情况调整的限制。我们研究这些局限性可以通过人工神经网络克服在多大程度上，以及需要对神经网络模型的特定域特异性优化才能优于常规过滤器解决方案。具有基于标记的光学基础真相的各种运动记录用于性能评估和比较。仅当引入域特异性优化时，提出的神经网络才能在所有动作上胜过常规过滤器。我们得出的结论是，它们是基于惯性传感器的实时态度估计的有前途的工具，但是需要专家知识和丰富的数据集来实现最佳性能。
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.
