• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Information Fusion >Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation
【24h】

Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation

机译：神经网络与常规滤波器的基于惯性传感器的姿态估计

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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.
机译：惯性测量单位通常用于估计运动物体的姿态。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，当考虑到大范围的不同的动态和静态旋转和平移运动时，由于需要根据情况调整加速度计和陀螺仪融合权重，因此可达到的精度受到限制。我们研究了在多大程度上可以通过人工神经网络克服这些局限性，以及要胜过常规过滤器解决方案需要多少特定于域的神经网络模型优化。具有基于标记的光学地面真相的各种运动记录用于性能评估和比较。仅在引入特定领域的优化后，才能发现拟议的神经网络在所有运动中均优于常规滤波器。我们得出的结论是，它们是基于惯性传感器的实时姿态估计的有前途的工具，但是要获得最佳性能，既需要专家知识，又需要丰富的数据集。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号