# An Integrated LSTM-Rule-Based Fusion Method for the Localization of Intelligent Vehicles in a Complex Environment

## TL;DR

This paper introduces a new vehicle localization method combining GPS, laser SLAM, and an odometer with LSTM networks to achieve centimeter-level accuracy in complex environments.

## Contribution

A novel fusion method using fuzzy rules, trajectory matching, and Dual-LSTM networks for robust vehicle localization in complex settings.

## Key findings

- The proposed method achieved centimeter-level localization accuracy.
- It reduced the average root mean square error by 66% compared to EKF methods.
- The system was validated through long-term real vehicle platform testing.

## Abstract

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.