Neural Augmented Kalman Filters for Road Network assisted GNSS positioning
By: Hans van Gorp , Davide Belli , Amir Jalalirad and more
Potential Business Impact:
Makes GPS work better in cities.
The Global Navigation Satellite System (GNSS) provides critical positioning information globally, but its accuracy in dense urban environments is often compromised by multipath and non-line-of-sight errors. Road network data can be used to reduce the impact of these errors and enhance the accuracy of a positioning system. Previous works employing road network data are either limited to offline applications, or rely on Kalman Filter (KF) heuristics with little flexibility and robustness. We instead propose training a Temporal Graph Neural Network (TGNN) to integrate road network information into a KF. The TGNN is designed to predict the correct road segment and its associated uncertainty to be used in the measurement update step of the KF. We validate our approach with real-world GNSS data and open-source road networks, observing a 29% decrease in positioning error for challenging scenarios compared to a GNSS-only KF. To the best of our knowledge, ours is the first deep learning-based approach jointly employing road network data and GNSS measurements to determine the user position on Earth.
Similar Papers
LF-GNSS: Towards More Robust Satellite Positioning with a Hard Example Mining Enhanced Learning-Filtering Deep Fusion Framework
Robotics
Makes self-driving cars find their way better.
Enhancing Steering Estimation with Semantic-Aware GNNs
CV and Pattern Recognition
Cars steer better using 3D pictures, not just 2D.
Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework
Machine Learning (CS)
Predicts traffic jams better, even with bad weather.