Why semantics matters: A deep study on semantic particle-filtering
  localization in a LiDAR semantic pole-map
Yuming Huang Yi Gu Chengzhong Xu Hui Kong
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.