Georeferenced Visual SLAM

Abstract

This thesis presents a complementary localization solution for taxis and ride-hailing operators in situations where GNSS is unavailable or unreliable. The proposed method leverages monocular visual SLAM techniques, specifically the ORB-SLAM 3 library, to create a map of the environment and localize within it. The system uses a car-mounted camera for image capture and an advanced GNSS receiver to record accurate ground truth. This data is then used as input for training a deep learning model to transform SLAM coordinates into georeferenced coordinates. The thesis explores different approaches to solving the coordinate transformation problem, including linear transformation, machine learning regression algorithms, and deep learning with neural networks. Results show that the deep learning based approach provides the best localization accuracy, surpassing that of modern smartphone GNSS. The study contributes a practical solution for real-time localization for ride-hailing operators when GNSS is compromised, with the potential for future implementation using smartphone cameras.