# Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning

arXiv: https://arxiv.org/abs/2001.09438 · HTML: https://mlsanity.com/paper/2001.09438/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Probabilistic Visual Place Recognition for Hierarchical Localization](https://mlsanity.com/paper/2105.03091/) (2021)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [STUN: Self-Teaching Uncertainty Estimation for Place Recognition](https://mlsanity.com/paper/2203.01851/) (2022)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Open-RadVLAD: Fast and Robust Radar Place Recognition](https://mlsanity.com/paper/2401.15380/) (2024)
- [Contrastive Learning for Unsupervised Radar Place Recognition](https://mlsanity.com/paper/2110.02744/) (2021)
- [Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities](https://mlsanity.com/paper/2112.04282/) (2021)
- [Large-Scale Topological Radar Localization Using Learned Descriptors](https://mlsanity.com/paper/2110.03081/) (2021)
- [LRFusionPR: A Polar BEV-Based LiDAR-Radar Fusion Network for Place Recognition](https://mlsanity.com/paper/2504.19186/) (2025)
- [Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos](https://mlsanity.com/paper/2106.06703/) (2021)
- [RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline](https://mlsanity.com/paper/2309.07094/) (2023)
- [VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.09025/) (2024)
- [RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving](https://mlsanity.com/paper/2603.07920/) (2026)
- [4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation](https://mlsanity.com/paper/2603.26206/) (2026)

## Abstract (arXiv)

This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction for more suitability to the polar nature of radar scan formation using cylindrical convolutions, anti-aliasing blurring, and azimuth-wise max-pooling; all in order to bolster the rotational invariance. The enforced metric space is then used to encode a reference trajectory, serving as a map, which is queried for nearest neighbours (NNs) for recognition of places at run-time. We demonstrate the performance of our topological localisation system over the course of many repeat forays using the largest radar-focused mobile autonomy dataset released to date, totalling 280 km of urban driving, a small portion of which we also use to learn the weights of the modified architecture. As this work represents a novel application for FMCW radar, we analyse the utility of the proposed method via a comprehensive set of metrics which provide insight into the efficacy when used in a realistic system, showing improved performance over the root architecture even in the face of random rotational perturbation.