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 D2SLAM: a decentralized and distributed (D2) collaborative SLAM
algorithm. This algorithm has high local accuracy and global consistency, and
the distributed architecture allows it to scale up. D2SLAM 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 D2 goal.
D2SLAM is robust to transient loss of communication, network delays, and
other factors. Thanks to the flexible architecture, D2SLAM has the potential
of applying in various scenarios