SemanticCSLAM: Using Environment Landmarks for Cooperative Simultaneous Localization and Mapping

To improve the accuracy and efficiency of LiDAR mapping, cooperative simultaneous localization and mapping (SLAM) has been considered for complex large scenes. Recognizing the same positions and detecting global loop closures are important for achieving cooperative SLAM. However, most of the current position recognition and loop closure detection methods are based on images or point clouds. These methods may make mistakes if structures or textures are similar. To overcome this problem, we propose SemanticCSLAM, which is a Cooperative SLAM system that uses environment semantic landmarks for position recognition and loop closure detection. The proposed SemanticCSLAM consistes of a single SLAM module based on A-LOAM, a trajectory alignment module and a global optimization module based on environment landmarks. Through the inertial measurement unit (IMU) carried by the agent, such as an unmanned ground vehicle (UGV), the environment landmarks can be detected. Based on these environment landmarks, the alignment module aligns trajectories from different agents. Finally, the loop closure detection and optimization module performs loop closure detection and global optimization based on these environment landmarks. We collected a dataset, which contains indoor and outdoor data, for testing. These experimental results in different scenes show that the environment landmarks can effectively improve the performance of cooperative SLAM systems.