Large-Scale Topological Radar Localization Using Learned Descriptors
This work addresses localization for autonomous driving systems, presenting an incremental improvement by applying learned descriptors to radar data.
The authors tackled large-scale topological localization using radar scan images by developing a deep network to compute rotationally invariant global descriptors, achieving experimental evaluation on the MulRan and Oxford Radar RobotCar datasets and comparing radar-based to LiDAR-based methods.
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors. We present a simple yet efficient deep network architecture to compute a rotationally invariant discriminative global descriptor from a radar scan image. The performance and generalization ability of the proposed method is experimentally evaluated on two large scale driving datasets: MulRan and Oxford Radar RobotCar. Additionally, we present a comparative evaluation of radar-based and LiDAR-based localization using learned global descriptors. Our code and trained models are publicly available on the project website.