Predictive machine learning-based error correction in GPS/IMU localization to improve navigation of autonomous vehicles

Abstract

Precise localization is crucial for the safety-critical factor and effective navigation of autonomous vehicles. This applied research examines machine learning models’ use to estimate, predict and correct errors in Global Positioning System (GPS)/ Inertial Measurement Unit (IMU) localization for autonomous vehicles indoors and outdoors applications. This ongoing development aims to improve localization accuracy 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 the model with the highest performance is determined by evaluating the Mean Squared Error (MSE) values.