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In most urban and suburban areas, pole-like structures such as tree trunks orutility poles are ubiquitous. These structural landmarks are very useful forthe localization of autonomous vehicles given their geometrical locations inmaps and measurements from sensors. In this work, we aim at creating anaccurate map for autonomous vehicles or robots with pole-like structures as thedominant localization landmarks, hence called pole-map. In contrast to theprevious pole-based mapping or localization methods, we exploit the semanticsof pole-like structures. Specifically, semantic segmentation is achieved by anew mask-range transformer network in a mask-classfication paradigm. With thesemantics extracted for the pole-like structures in each frame, a multi-layersemantic pole-map is created by aggregating the detected pole-like structuresfrom all frames. Given the semantic pole-map, we propose a semanticparticle-filtering localization scheme for vehicle localization. Theoretically,we have analyzed why the semantic information can benefit the particle-filterlocalization, and empirically it is validated on the public SemanticKITTIdataset that the particle-filtering localization with semantics achieves muchbetter performance than the counterpart without semantics when each particle'sodometry prediction and/or the online observation is subject to uncertaintiesat significant levels.

Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map