A Novel Deep Odometry Network for Vehicle Positioning Based on Smartphone Article Swipe
Jingxian Wang
,
Duojie Weng
,
Xuanyu Qu
,
Weihao Ding
,
Wu Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2023.3240227
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2023.3240227
Smartphone with multiple sensors integration has been widely used for navigation. The inertial measurement unit (IMU) embedded in smartphones has been widely used for pedestrian navigation for counting steps. However, it is a challenge to measure the accurate velocity of the vehicle from the smartphone-embedded IMU data with a high noise level. Thus, current vehicle navigation with a smartphone relies substantially on the Global Navigation Satellite System (GNSS), which provides unreliable positions in urban dense areas due to the blockage and the reflection of GNSS signals. In this study, we propose a smartphone-based positioning method to improve vehicle positioning performance continuously in GNSS-degraded areas through the improvement of IMU velocity estimation. A convolutional neural network–gated recurrent unit (CNN-GRU) combined deep learning odometry network, termed DeepOdo, is proposed to estimate the velocity of the vehicle with the IMU and barometer data as the input, rather than the traditional integral of the IMU measurements. Raw sensor data is utilized to boost the robustness. Labels of the DeepOdo are obtained from the integrated GNSS/IMU/barometer solutions in the smartphone which significantly simplifies the dataset collection. In GNSS-denied areas, IMU, barometer, and DeepOdo are integrated to provide accurate navigation solutions for the vehicle. Results of the proposed method show 73.14% and 98.33% improvements in horizontal and vertical directions, respectively, compared with the non-holonomic constraints (NHCs) aided IMU. Finally, the DeepOdo network is deployed in Android smartphones to demonstrate that the proposed solution can work properly on the mobile platform.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tim.2023.3240227
- OA Status
- green
- Cited By
- 22
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319990415
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319990415Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tim.2023.3240227Digital Object Identifier
- Title
-
A Novel Deep Odometry Network for Vehicle Positioning Based on SmartphoneWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Jingxian Wang, Duojie Weng, Xuanyu Qu, Weihao Ding, Wu ChenList of authors in order
- Landing page
-
https://doi.org/10.1109/tim.2023.3240227Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://ira.lib.polyu.edu.hk/bitstream/10397/99370/1/Wang_Novel_Deep_Odometry.pdfDirect OA link when available
- Concepts
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Inertial measurement unit, GNSS applications, Odometry, Computer science, Artificial intelligence, Real-time computing, Computer vision, Android (operating system), Barometer, Global Positioning System, Mobile robot, Geography, Telecommunications, Robot, Operating system, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 12, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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