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.

翻译：最先进的激光雷达地点识别模型在测试环境与训练数据集不同时，表现出不可靠的性能，这限制了其在复杂且动态变化环境中的应用。为解决这一问题，我们研究了认知不确定性感知的激光雷达地点识别任务，其中每个预测的地点必须附带相关的不确定性，用于识别和拒绝错误预测。我们引入了一种新的评估协议，并针对该任务提出了首个综合性基准，测试了五种不确定性估计技术与三个大规模数据集。结果表明，集成方法（Ensembles）是性能最佳的技术，能够在新型环境中持续提升激光雷达地点识别与不确定性估计的性能，尽管其计算成本较高。代码已公开于 https://github.com/csiro-robotics/Uncertainty-LPR。