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.

该论文介绍了一种名为 SAGE-ICP 的新型语义信息辅助 ICP 方法，通过三维卷积网络提取全扫描的及时有效的语义信息，并将这些点级标签深入应用于配准的各个部分，包括语义体素下采样、数据关联、自适应局部地图和动态车辆去除，实验证明该方法在大规模场景中可以提高定位的准确性，即使语义信息存在一定误差，同时保持实时性能，即快于传感器帧速率。