The availability of a reliable map and a robust localization system is critical for the operation of an autonomous vehicle. In a modern system, both mapping and localization solutions generally employ convolutional neural network (CNN) --based perception. Hence, any algorithm should consider potential errors in perception for safe and robust functioning. In this work, we present uncertainty-aware panoptic Localization and Mapping (uPLAM), which employs perception uncertainty as a bridge to fuse the perception information with classical localization and mapping approaches. We introduce an uncertainty-based map aggregation technique to create a long-term panoptic bird's eye view map and provide an associated mapping uncertainty. Our map consists of surface semantics and landmarks with unique IDs. Moreover, we present panoptic uncertainty-aware particle filter-based localization. To this end, we propose an uncertainty-based particle importance weight calculation for the adaptive incorporation of perception information into localization. We also present a new dataset for evaluating long-term panoptic mapping and map-based localization. Extensive evaluations showcase that our proposed uncertainty incorporation leads to better mapping with reliable uncertainty estimates and accurate localization. We make our dataset and code available at: \url{http://uplam.cs.uni-freiburg.de}

翻译：统一的可靠地图和鲁棒定位系统对于自动驾驶车辆的操作至关重要。在现代系统中，建图和定位解决方案通常采用基于卷积神经网络（CNN）的感知。因此，任何算法都需考虑感知中的潜在误差，以确保安全且鲁棒的功能。本文提出不确定性感知的全景定位与建图（uPLAM），该方法利用感知不确定性作为桥梁，将感知信息与经典定位和建图方法相融合。我们引入一种基于不确定性的地图聚合技术，以创建长期的全景鸟瞰地图，并提供相应的建图不确定性。该地图包含带有唯一标识符的表面语义和路标。此外，我们提出基于全景不确定性感知粒子滤波的定位方法。为此，我们设计了一种基于不确定性的粒子重要性权重计算方法，用于将感知信息自适应地融入定位过程。我们还提供了一个新数据集，用于评估长期全景建图和基于地图的定位。大量评估表明，我们提出的不确定性融合方法能够实现更优的建图（具有可靠的不确定性估计）和精确的定位。我们在以下网址提供数据集和代码：\url{http://uplam.cs.uni-freiburg.de}