论文标题
不确定性感知的激光雷达场所在新颖环境中识别
Uncertainty-Aware Lidar Place Recognition in Novel Environments
论文作者
论文摘要
在与训练数据集不同的环境中进行测试时，最先进的激光雷达场所识别模型表现出不可靠的性能，这限制了它们在复杂和不断发展的环境中的使用。为了解决这个问题，我们调查了不确定性感知的激光雷达场所识别的任务，其中每个预测的位置必须具有相关的不确定性，可用于识别和拒绝错误的预测。我们介绍了一种新颖的评估协议，并介绍了该任务的第一个综合基准，对五个不确定性估计技术和三个大规模数据集进行了测试。我们的结果表明，一种合奏方法是性能最高的技术，尽管它会产生计算成本，但仍一致地提高了新型环境中LIDAR位置识别和不确定性估计的性能。代码可在https://github.com/csiro-robotics/uncnertenty-lpr上公开获取。
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.
