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.Comment: submitted to the 2020 International Conference on Robotics and
Automation (ICRA