Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155329
Title: AbolDeepIO : a novel deep inertial odometry network for autonomous vehicles
Authors: Esfahani, Mahdi Abolfazli
Wang, Han
Wu, Keyu
Yuan, Shenghai
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Esfahani, M. A., Wang, H., Wu, K. & Yuan, S. (2020). AbolDeepIO : a novel deep inertial odometry network for autonomous vehicles. IEEE Transactions On Intelligent Transportation Systems, 21(5), 1941-1950. https://dx.doi.org/10.1109/TITS.2019.2909064
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: Inertial measurement units (IMUs) suffer from bias and measurement noise, which makes it much more complicated to tackle the problem of inertial odometry (IO). Due to the error propagation over time, while estimating robot position, an inaccurate estimation or a small error will cause the odometry and a localization system unreliable and unusable in a split of seconds. This paper presents a novel triple-channel deep IO network architecture based on the physical and mathematical models of IMUs. The proposed method simulates the noise model in the training phase and becomes robust to noise during testing. Besides, the proposed network architecture also considers the time interval between two consecutive IMU readings (sampling time) so that it is robust to the change of IMU frequency and the missing of IMU information. To the best of our knowledge, this paper is the first work reviewing and analyzing the existing IO methods used by the deep-learning-based visual-IO approaches. The proposed network architecture outperforms all the existing solutions on the IMU readings of the challenging Micro Aerial Vehicle dataset and improves the accuracy by approximately 25%.
URI: https://hdl.handle.net/10356/155329
ISSN: 1524-9050
DOI: 10.1109/TITS.2019.2909064
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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