Understanding the Behavior of Data-Driven Inertial Odometry with Kinematics-mimicking Deep Neural Network

Quentin Arnaud Dugnenhennequin, Hideaki Uchiyama, Joao Paulo Silva Do Monte Lima

研究成果: Contribution to journalArticle査読

抄録

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.

本文言語英語
ジャーナルIEEE Access
DOI
出版ステータス出版済み - 2021

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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