Uncertainty-Aware Lidar Place Recognition in Novel Environments

Keita Mason, Joshua Knights, Milad Ramezani, Peyman Moghadam, Dimity Miller

Published: 01 Jan 2023, Last Modified: 28 Jan 2026Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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.