D^2SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm
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In recent years, aerial swarm technology has developed rapidly. In order to accomplish a fully autonomous aerial swarm, a key technology is decentralized and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates the relative pose and the consistent global trajectories. In this paper, we propose D^2SLAM: a decentralized and distributed (D^2) collaborative SLAM algorithm. This algorithm has high local accuracy and global consistency, and the distributed architecture allows it to scale up. D^2SLAM covers swarm state estimation in two scenarios: near-field state estimation for high real-time accuracy at close range and far-field state estimation for globally consistent trajectories estimation at the long-range between UAVs. Distributed optimization algorithms are adopted as the backend to achieve the D^2 goal. D^2SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, D^2SLAM has the potential of applying in various scenarios.
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