Efficient Map Compression for Collaborative Visual SLAM
Swarm robotics is receiving increasing interest, because the collaborative completion of tasks, such as the exploration of unknown environments, leads to improved performance and reduced effort. The ability to exchange map information is an essential requirement for collaborative exploration. When moving to large-scale environments, where the communication data rate between the swarm participants is typically limited, efficient compression algorithms and an approach for discarding less informative parts of the map are key for a successful long-term operation. In this paper, we present a novel compression approach for environment maps obtained from a visual SLAM system. We apply feature coding to the visual information to compress the map efficiently. We make use of a minimum spanning tree to connect all features that serve as observations of a single map point. Thereby, we can exploit inter-feature dependencies and obtain an optimal coding order. Additionally, we add a map sparsification step to keep only useful map points by solving a linear integer programming problem, which preserves the map points that exhibit both good compression properties and high observability. We evaluate the proposed method on a standard dataset and show that our approach outperforms state-of-the-art techniques.