  Radar place recognition often involves encoding a live scan as a vector and
matching this vector to a database in order to recognise that the vehicle is in
a location that it has visited before. Radar is inherently robust to lighting
or weather conditions, but place recognition with this sensor is still affected
by: (1) viewpoint variation, i.e. translation and rotation, (2) sensor
artefacts or "noises". For 360-degree scanning radar, rotation is readily dealt
with by in some way aggregating across azimuths. Also, we argue in this work
that it is more critical to deal with the richness of representation and sensor
noises than it is to deal with translational invariance - particularly in urban
driving where vehicles predominantly follow the same lane when repeating a
route. In our method, for computational efficiency, we use only the polar
representation. For partial translation invariance and robustness to signal
noise, we use only a one-dimensional Fourier Transform along radial returns. We
also achieve rotational invariance and a very discriminative descriptor space
by building a vector of locally aggregated descriptors. Our method is more
comprehensively tested than all prior radar place recognition work - over an
exhaustive combination of all 870 pairs of trajectories from 30 Oxford Radar
RobotCar Dataset sequences (each approximately 10 km). Code and detailed
results are provided at github.com/mttgdd/open-radvlad, as an open
implementation and benchmark for future work in this area. We achieve a median
of 91.52% in Recall@1, outstripping the 69.55% for the only other open
implementation, RaPlace, and at a fraction of its computational cost (relying
on fewer integral transforms e.g. Radon, Fourier, and inverse Fourier).
