  Localization on 3D data is a challenging task for unmanned vehicles,
especially in long-term dynamic urban scenarios. Due to the generality and
long-term stability, the pole-like objects are very suitable as landmarks for
unmanned vehicle localization in time-varing scenarios. In this paper, a
long-term LiDAR-only localization algorithm based on semantic cluster map is
proposed. At first, the Convolutional Neural Network(CNN) is used to infer the
semantics of LiDAR point clouds. Combined with the point cloud segmentation,
the long-term static objects pole/trunk in the scene are extracted and
registered into a semantic cluster map. When the unmanned vehicle re-enters the
environment again, the relocalization is completed by matching the clusters of
the local map with the clusters of the global map. Furthermore, the continuous
matching between the local and global maps stably outputs the global pose at
2Hz to correct the drift of the 3D LiDAR odometry. The proposed approach
realizes localization in the long-term scenarios without maintaining the
high-precision point cloud map. The experimental results on our campus dataset
demonstrate that the proposed approach performs better in localization accuracy
compared with the current state-of-the-art methods. The source of this paper is
available at: http://www.github.com/HITSZ-NRSL/long-term-localization.
