MEMS Inertial Measurement Units (IMUs) are inexpensive and effective sensors that provide proprioceptive motion measurements for many robots and consumer devices. However, their noise characteristics and manufacturing imperfections lead to complex ramifications in classical fusion pipelines. While deep learning models provide the required flexibility to model these complexities from data, they have higher computation and memory requirements, making them impractical choices for low-power and embedded applications. This paper attempts to address the mentioned conflict by proposing a computationally, efficient inertial representation for deep inertial odometry. Replacing the raw IMU data in deep Inertial models, preintegrated features improves the model's efficiency. The effectiveness of this method has been demonstrated for the task of pedestrian inertial odometry, and its efficiency has been shown through its embedded implementation on a microcontroller with restricted resources.

中文翻译：

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用于高效深度惯性里程计的预集成 IMU 功能

MEMS 惯性测量单元 (IMU) 是廉价且有效的传感器，可为许多机器人和消费设备提供本体感觉运动测量。然而，它们的噪声特性和制造缺陷会导致经典融合管道的复杂后果。虽然深度学习模型提供了从数据建模这些复杂性所需的灵活性，但它们具有更高的计算和内存要求，这使得它们对于低功耗和嵌入式应用程序来说是不切实际的选择。本文试图通过为深度惯性里程计提出一种计算有效的惯性表示来解决上述冲突。替换深度惯性模型中的原始 IMU 数据，预集成功能提高了模型的效率。