  State-of-the-art lidar place recognition models exhibit unreliable
performance when tested on environments different from their training dataset,
which limits their use in complex and evolving environments. To address this
issue, we investigate the task of uncertainty-aware lidar place recognition,
where each predicted place must have an associated uncertainty that can be used
to identify and reject incorrect predictions. We introduce a novel evaluation
protocol and present the first comprehensive benchmark for this task, testing
across five uncertainty estimation techniques and three large-scale datasets.
Our results show that an Ensembles approach is the highest performing
technique, consistently improving the performance of lidar place recognition
and uncertainty estimation in novel environments, though it incurs a
computational cost. Code is publicly available at
https://github.com/csiro-robotics/Uncertainty-LPR.
