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...
Saved in:
Main Authors: | Yuan, Quan, Yan, Fuwu, Yin, Zhishuai, Lv, Chen, Hu, Jie, Li, Yue, Wang, Jinhai |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181760 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Intelligent sensor fusion and learning for autonomous robot navigation
by: Tan, K.C., et al.
Published: (2014) -
Genetically evolved fuzzy rule-based classifiers and application to automotive classification
by: Chua, T.W., et al.
Published: (2014) -
A rule self-regulating fuzzy controller
by: Qiao, W.Z., et al.
Published: (2014) -
Mining of gradual rules
by: CHEN ZHENG
Published: (2010) -
Flexible dispatching rules for automated guided vehicles based on a self-adapting fuzzy prioritizing system
by: Tan, K.K., et al.
Published: (2014)