  LiDAR-based SLAM system is admittedly more accurate and stable than others,
while its loop closure detection is still an open issue. With the development
of 3D semantic segmentation for point cloud, semantic information can be
obtained conveniently and steadily, essential for high-level intelligence and
conductive to SLAM. In this paper, we present a novel semantic-aided LiDAR SLAM
with loop closure based on LOAM, named SA-LOAM, which leverages semantics in
odometry as well as loop closure detection. Specifically, we propose a
semantic-assisted ICP, including semantically matching, downsampling and plane
constraint, and integrates a semantic graph-based place recognition method in
our loop closure detection module. Benefitting from semantics, we can improve
the localization accuracy, detect loop closures effectively, and construct a
global consistent semantic map even in large-scale scenes. Extensive
experiments on KITTI and Ford Campus dataset show that our system significantly
improves baseline performance, has generalization ability to unseen data and
achieves competitive results compared with state-of-the-art methods.
