论文标题
IMUNET：IMU导航和定位的有效回归体系结构
IMUNet: Efficient Regression Architecture for IMU Navigation and Positioning
论文作者
论文摘要
近年来，基于数据驱动的导航和定位方法吸收了注意力，并且在准确性和效率方面，它的表现优于其所有竞争对手方法。本文介绍了一种名为IMUNET的新体系结构，该架构是对边缘设备实现的位置估算的准确效率，以接收一系列RAW IMU测量结果。该体系结构已与最新的CNN网络的一维版本进行了比较，这些版本是在准确性和效率方面引入的，用于边缘设备实现。此外，已经提出了一种使用IMU传感器和Google Arcore API收集数据集的新方法，并已记录了公开可用的数据集。使用四个不同的数据集以及提出的数据集和实际设备实现的全面评估已经证明了体系结构的性能。 Pytorch和Tensorflow框架以及Android应用程序代码中的所有代码都已共享，以改善进一步的研究。
Data-driven based method for navigation and positioning has absorbed attention in recent years and it outperforms all its competitor methods in terms of accuracy and efficiency. This paper introduces a new architecture called IMUNet which is accurate and efficient for position estimation on edge device implementation receiving a sequence of raw IMU measurements. The architecture has been compared with one dimension version of the state-of-the-art CNN networks that have been introduced recently for edge device implementation in terms of accuracy and efficiency. Moreover, a new method for collecting a dataset using IMU sensors on cell phones and Google ARCore API has been proposed and a publicly available dataset has been recorded. A comprehensive evaluation using four different datasets as well as the proposed dataset and real device implementation has been done to prove the performance of the architecture. All the code in both Pytorch and Tensorflow framework as well as the Android application code have been shared to improve further research.
