  Semantic SLAM is an important field in autonomous driving and intelligent
agents, which can enable robots to achieve high-level navigation tasks, obtain
simple cognition or reasoning ability and achieve language-based
human-robot-interaction. In this paper, we built a system to creat a semantic
3D map by combining 3D point cloud from ORB SLAM with semantic segmentation
information from Convolutional Neural Network model PSPNet-101 for large-scale
environments. Besides, a new dataset for KITTI sequences has been built, which
contains the GPS information and labels of landmarks from Google Map in related
streets of the sequences. Moreover, we find a way to associate the real-world
landmark with point cloud map and built a topological map based on semantic
map.
