  LiDAR place recognition approaches based on deep learning suffer from
significant performance degradation when there is a shift between the
distribution of training and test datasets, often requiring re-training the
networks to achieve peak performance. However, obtaining accurate ground truth
data for new training data can be prohibitively expensive, especially in
complex or GPS-deprived environments. To address this issue we propose
GeoAdapt, which introduces a novel auxiliary classification head to generate
pseudo-labels for re-training on unseen environments in a self-supervised
manner. GeoAdapt uses geometric consistency as a prior to improve the
robustness of our generated pseudo-labels against domain shift, improving the
performance and reliability of our Test-Time Adaptation approach. Comprehensive
experiments show that GeoAdapt significantly boosts place recognition
performance across moderate to severe domain shifts, and is competitive with
fully supervised test-time adaptation approaches. Our code is available at
https://github.com/csiro-robotics/GeoAdapt.
