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.

翻译：调频连续波雷达因其在恶劣环境中的固有能力而在地点识别领域受到日益关注。然而，雷达测量过程中的复杂伪影需要适当的置信度估计，以确保这一新兴传感器模态的安全可靠应用。本研究提出一种多会话地图管理系统，该系统基于嵌入空间中学习到的方差特性，构建用于后续定位的最优地图。利用相同的方差特性，我们还提出一种新的内省式拒绝机制，可主动规避可能错误的定位查询。为此，我们引入鲁棒的噪声感知度量学习，该方法既能利用沿行驶路径雷达数据的短时变异性进行数据增强，又能预测基于度量空间的地点识别中的下游不确定性。通过在牛津雷达机器人车与MulRan数据集上的广泛交叉验证测试，我们证明了该方法的有效性。实验表明，在仅使用单个最近邻查询的情况下，我们的方法超越了当前最先进的雷达地点识别方法及其他感知不确定性方法。我们还展示了在困难测试环境中基于不确定性拒绝查询时性能的持续提升，这一特性在对比的竞争性感知不确定性地点识别系统中未能观察到。