LCCD-SLAM: A Low-Bandwidth Centralized Collaborative Direct Monocular SLAM for Multi-Robot Collaborative Mapping

Abstract

In this paper, we present a low-bandwidth centralized collaborative direct monocular SLAM (LCCD-SLAM) for multi-robot systems collaborative mapping. Each agent runs the direct method-based visual odometry (VO) independently, giving the algorithm the advantages of semi-dense point cloud reconstruction and robustness in the featureless regions. The agent sends the server mature keyframes marginalized from the sliding window, which greatly reduces the bandwidth requirement. In the server, we adopt the point selection strategy of LDSO, use the Bag of Words (BoW) model to detect the loop closure candidate frames, and effectively reduce the accumulative drift of global rotation, translation and scale through pose graph optimization. Map matching is responsible for detecting trajectory overlap between agents and merging the two overlapping submaps into a new map. The proposed approach is evaluated on publicly available datasets and real-world experiments, which demonstrates its ability to perform collaborative point cloud mapping in a multi-agent system.