  Robust and accurate pose estimation in unknown environments is an essential
part of robotic applications. We focus on LiDAR-based point-to-point ICP
combined with effective semantic information. This paper proposes a novel
semantic information-assisted ICP method named SAGE-ICP, which leverages
semantics in odometry. The semantic information for the whole scan is timely
and efficiently extracted by a 3D convolution network, and these point-wise
labels are deeply involved in every part of the registration, including
semantic voxel downsampling, data association, adaptive local map, and dynamic
vehicle removal. Unlike previous semantic-aided approaches, the proposed method
can improve localization accuracy in large-scale scenes even if the semantic
information has certain errors. Experimental evaluations on KITTI and KITTI-360
show that our method outperforms the baseline methods, and improves accuracy
while maintaining real-time performance, i.e., runs faster than the sensor
frame rate.
