Uncertainty-Aware Lidar Place Recognition in Novel Environments
This work addresses a critical limitation for robotics and autonomous systems that operate in complex, evolving environments, but it is incremental as it builds on existing uncertainty estimation techniques.
The paper tackles the problem of unreliable lidar place recognition in novel environments by introducing uncertainty-aware methods, showing that an Ensembles approach improves performance and uncertainty estimation across three datasets, though with increased computational cost.
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.