Predictive Machine Learning-Based Error Correction in GPS/IMU Localization to Improve Navigation of Autonomous Vehicles
Conference paper
Onyema, U. and Shafik, M. 2024. Predictive Machine Learning-Based Error Correction in GPS/IMU Localization to Improve Navigation of Autonomous Vehicles. 21st International Conference on Manufacturing Research . Glasgow 28 - 30 Aug 2024 https://doi.org/10.1051/matecconf/202440112004
| Authors | Onyema, U. and Shafik, M. |
|---|---|
| Type | Conference paper |
| Abstract | Precise localization is crucial for the safety critical factor and effective navigation of autonomous vehicles. This applied research work examines the use of machine learning models to estimate, predict and correct errors in Global positioning System (GPS)/ Inertial Measurement Unit (IMU) localization for autonomous vehicles indoors and outdoors applications. The objective of this ongoing development is to improve the accuracy of localization by utilizing exploratory data analysis (EDA) and implementing models such as linear regression, random forest regressor, and decision tree regressor. The assessment is performed with the mean squared error (MSE) metric, yielding values of 1.7069427028104143e-05 for the decision tree, linear regression, and random forest models. The results showed that model with the highest performance is determined by the evaluation carried out of the Mean Squared Error (MSE) values. |
| Keywords | Predictive Machine Learning, Error Correction in GPS/IMU Localization, Navigation of Autonomous Vehicles. |
| Year | 2024 |
| Conference | 21st International Conference on Manufacturing Research |
| Digital Object Identifier (DOI) | https://doi.org/10.1051/matecconf/202440112004 |
| Web address (URL) | https://www.icmr.org.uk/ |
| Accepted author manuscript | File Access Level Restricted |
| Publisher's version | License File Access Level Open |
| Journal citation | 401 |
| File | File Access Level Restricted |
| Output status | Published |
| Publication dates | |
| Online | 01 Oct 2024 |
| Publication process dates | |
| Deposited | 27 Oct 2025 |
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