In most urban and suburban areas, pole-like structures such as tree trunks or utility poles are ubiquitous. These structural landmarks are very useful for the localization of autonomous vehicles given their geometrical locations in maps and measurements from sensors. In this work, we aim at creating an accurate map for autonomous vehicles or robots with pole-like structures as the dominant localization landmarks, hence called pole-map. In contrast to the previous pole-based mapping or localization methods, we exploit the semantics of pole-like structures. Specifically, semantic segmentation is achieved by a new mask-range transformer network in a mask-classfication paradigm. With the semantics extracted for the pole-like structures in each frame, a multi-layer semantic pole-map is created by aggregating the detected pole-like structures from all frames. Given the semantic pole-map, we propose a semantic particle-filtering localization scheme for vehicle localization. Theoretically, we have analyzed why the semantic information can benefit the particle-filter localization, and empirically it is validated on the public SemanticKITTI dataset that the particle-filtering localization with semantics achieves much better performance than the counterpart without semantics when each particle's odometry prediction and/or the online observation is subject to uncertainties at significant levels.

翻译：在大多数城市和郊区环境中，诸如树干或电线杆等杆状结构普遍存在。鉴于这些结构在地图中的几何位置以及传感器测量数据，它们对自动驾驶车辆的定位非常有用。本研究旨在创建以杆状结构为主要定位地标的自动驾驶车辆或机器人的精确地图，因此称为杆状地图。与以往基于杆状结构的建图或定位方法不同，我们利用了杆状结构的语义信息。具体而言，语义分割通过一种新的掩码-范围变换网络在掩码分类范式中实现。通过提取每帧中杆状结构的语义信息，聚合所有帧中检测到的杆状结构，创建了多层语义杆状地图。基于该语义杆状地图，我们提出了一种用于车辆定位的语义粒子滤波定位方案。理论上，我们分析了语义信息为何能提升粒子滤波定位性能，并通过公共SemanticKITTI数据集进行实证验证：当每个粒子的里程计预测和/或在线观测存在显著不确定性时，采用语义信息的粒子滤波定位性能远优于不含语义信息的对应方法。