Georeferenced Visual SLAM
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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.
Description
Keywords
Visual SLAM, ORB-SLAM 3, georeferencing, neural networks