Off the Radar: Uncertainty-Aware Radar Place Recognition with Introspective Querying and Map Maintenance

TL;DR

This paper introduces an uncertainty-aware radar place recognition system that uses learned variance properties for map management and query rejection, improving accuracy and reliability in challenging environments.

Contribution

It presents a novel multi-session map management and introspective query rejection method leveraging robust noise-aware metric learning for radar localization.

Findings

Outperforms state-of-the-art in radar place recognition on Oxford Radar RobotCar and MulRan datasets.

Effectively rejects uncertain queries, increasing performance in difficult environments.

Demonstrates the importance of uncertainty estimation for reliable radar-based localization.

Abstract

Localisation with Frequency-Modulated Continuous-Wave (FMCW) radar has gained increasing interest due to its inherent resistance to challenging environments. However, complex artefacts of the radar measurement process require appropriate uncertainty estimation to ensure the safe and reliable application of this promising sensor modality. In this work, we propose a multi-session map management system which constructs the best maps for further localisation based on learned variance properties in an embedding space. Using the same variance properties, we also propose a new way to introspectively reject localisation queries that are likely to be incorrect. For this, we apply robust noise-aware metric learning, which both leverages the short-timescale variability of radar data along a driven path (for data augmentation) and predicts the downstream uncertainty in metric-space-based place recognition. We prove the effectiveness of our method over extensive cross-validated tests of the Oxford Radar RobotCar and MulRan dataset. In this, we outperform the current state-of-the-art in radar place recognition and other uncertainty-aware methods when using only single nearest-neighbour queries. We also show consistent performance increases when rejecting queries based on uncertainty over a difficult test environment, which we did not observe for a competing uncertainty-aware place recognition system.