  Place recognition gives a SLAM system the ability to correct cumulative
errors. Unlike images that contain rich texture features, point clouds are
almost pure geometric information which makes place recognition based on point
clouds challenging. Existing works usually encode low-level features such as
coordinate, normal, reflection intensity, etc., as local or global descriptors
to represent scenes. Besides, they often ignore the translation between point
clouds when matching descriptors. Different from most existing methods, we
explore the use of high-level features, namely semantics, to improve the
descriptor's representation ability. Also, when matching descriptors, we try to
correct the translation between point clouds to improve accuracy. Concretely,
we propose a novel global descriptor, Semantic Scan Context, which explores
semantic information to represent scenes more effectively. We also present a
two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align
the point cloud to improve matching performance. Our experiments on the KITTI
dataset show that our approach outperforms the state-of-the-art methods with a
large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.
