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.

本文旨在利用具有地标形状的柱状物作为主导式定位地标，创造出一张准确的用于自主车辆或机器人的地图，即 Pole-map。通过利用柱状物的语义信息，在所有帧中检测和聚合检测到的柱状物，创造出一个多层语义 Pole-map，并提出了一个基于语义 Pole-map 的粒子滤波定位方案。经理论分析和公共数据集实验结果表明，本文所提出的基于语义的定位方法比不使用语义的方法在粒子滤波定位中表现更好。