Large-Scale Topological Radar Localization Using Learned Descriptors

TL;DR

This paper introduces a deep learning approach for large-scale topological localization using radar images, producing rotationally invariant descriptors that improve localization accuracy and generalization across datasets.

Contribution

The paper presents a novel deep network architecture for radar scan descriptors, enabling effective large-scale topological localization and comparison with LiDAR-based methods.

Findings

Effective radar-based localization demonstrated on large datasets

Rotationally invariant descriptors improve robustness

Comparable or superior performance to LiDAR-based methods

Abstract

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.