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 $D^2$SLAM: 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. $D^2$SLAM 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. $D^2$SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, $D^2$SLAM has the potential of applying in various scenarios.

翻译：近年来,空中群温技术迅速发展,为了实现完全自主的空中群温,一种关键技术是分散和分散的,用于空中群温的SLAM(CSLAM)协作,估计相对面貌和全球轨迹的一致性。在本文中,我们提议2D2$SLAM:分散和分布的SLAM合作算法。这一算法具有很高的当地准确性和全球一致性,分布式结构使其能够扩大规模。2D2$SLAM在两种情况下覆盖了Sarm国家估计:近地点和远地点国家高实时精度估计,在UAVs之间的长距离全球一致轨迹估计。采用分散式优化算法作为实现2D2美元目标的后端。2D2$SLAM对于通信的短暂损失、网络延误和其他因素非常有力。由于结构灵活,$D2SLAM具有应用于各种情景的潜力。