An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment
To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localiza...
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sg-ntu-dr.10356-1817602024-12-17T00:42:09Z An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment Yuan, Quan Yan, Fuwu Yin, Zhishuai Lv, Chen Hu, Jie Li, Yue Wang, Jinhai School of Mechanical and Aerospace Engineering The Automated Driving and Human-Machine System Group Engineering Multi-source fusion Fuzzy rules To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy. Published version This work is funded by the Major Program (JD) of Hubei Province (2023BAA017). 2024-12-17T00:42:09Z 2024-12-17T00:42:09Z 2024 Journal Article Yuan, Q., Yan, F., Yin, Z., Lv, C., Hu, J., Li, Y. & Wang, J. (2024). An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment. Sensors, 24(12), 4025-. https://dx.doi.org/10.3390/s24124025 1424-8220 https://hdl.handle.net/10356/181760 10.3390/s24124025 38931808 2-s2.0-85197160326 12 24 4025 en Sensors © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
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Engineering Multi-source fusion Fuzzy rules Yuan, Quan Yan, Fuwu Yin, Zhishuai Lv, Chen Hu, Jie Li, Yue Wang, Jinhai An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
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To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Yuan, Quan Yan, Fuwu Yin, Zhishuai Lv, Chen Hu, Jie Li, Yue Wang, Jinhai |
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Article |
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Yuan, Quan Yan, Fuwu Yin, Zhishuai Lv, Chen Hu, Jie Li, Yue Wang, Jinhai |
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Yuan, Quan |
title |
An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
title_short |
An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
title_full |
An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
title_fullStr |
An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
title_full_unstemmed |
An integrated LSTM-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
title_sort |
integrated lstm-rule-based fusion method for the localization of intelligent vehicles in a complex environment |
publishDate |
2024 |
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https://hdl.handle.net/10356/181760 |
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1819113049424920576 |