Understanding the Behavior of Data-Driven Inertial Odometry With Kinematics-Mimicking Deep Neural Network
Understanding the Behavior of Data-Driven Inertial Odometry With Kinematics-Mimicking Deep Neural Network
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DOI：
10.1109/access.2021.3062817
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Quentin Arnaud Dugne-Hennequin;Hideaki Uchiyama;João Paulo Silva Do Monte Lima
中科院分区：
文献类型：
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作者：
Quentin Arnaud Dugne-Hennequin;Hideaki Uchiyama;João Paulo Silva Do Monte Lima
在导航方面，最近仅使用来自低成本IMU的数据研究了惯性里程计（IO）的深度学习。使用深度神经网络（DNN）估计IO遭受的噪声，偏差和一些误差的测量，以实现更准确的姿态估计。虽然关于这个主题的许多研究强调了他们方法的性能，但DNN的数据驱动IO的行为还没有得到澄清。因此，本文从多个方面对基于动态模拟的DNN IO进行了定量分析。首先，新的网络架构旨在模拟运动学，并确保全面的分析。接下来，识别与IO高度相关的神经网络超参数。此外，他们的作用在性能进行了调查。在评估中，分析是使用公开的车辆和无人机IO数据集进行的。介绍这些结果是为了强调IO中剩余的问题，并被认为是促进进一步研究的指南。
In navigation, deep learning for inertial odometry (IO) has recently been investigated using data from a low-cost IMU only. The measurement of noise, bias, and some errors from which IO suffers is estimated with a deep neural network (DNN) to achieve more accurate pose estimation. While numerous studies on the subject highlighted the performances of their approach, the behavior of data-driven IO with DNN has not been clarified. Therefore, this paper presents a quantitative analysis of kinematics-mimicking DNN-based IO from various aspects. First, the new network architecture is designed to mimic the kinematics and ensure comprehensive analyses. Next, the hyper-parameters of neural networks that are highly correlated to IO are identified. Besides, their role in the performances is investigated. In the evaluation, the analyses were conducted with publicly-available IO datasets for vehicles and drones. The results are introduced to highlight the remaining problems in IO and are considered a guideline to promote further research.