  Place recognition is crucial for tasks like loop-closure detection and
re-localization. Single-chip millimeter wave radar (single-chip radar in short)
emerges as a low-cost sensor option for place recognition, with the advantage
of insensitivity to degraded visual environments. However, it encounters two
challenges. Firstly, sparse point cloud from single-chip radar leads to poor
performance when using current place recognition methods, which assume much
denser data. Secondly, its performance significantly declines in scenarios
involving rotational and lateral variations, due to limited overlap in its
field of view (FOV). We propose mmPlace, a robust place recognition system to
address these challenges. Specifically, mmPlace transforms intermediate
frequency (IF) signal into range azimuth heatmap and employs a spatial encoder
to extract features. Additionally, to improve the performance in scenarios
involving rotational and lateral variations, mmPlace employs a rotating
platform and concatenates heatmaps in a rotation cycle, effectively expanding
the system's FOV. We evaluate mmPlace's performance on the milliSonic dataset,
which is collected on the University of Science and Technology of China (USTC)
campus, the city roads surrounding the campus, and an underground parking
garage. The results demonstrate that mmPlace outperforms point cloud-based
methods and achieves 87.37% recall@1 in scenarios involving rotational and
lateral variations.
