THESIS
2022
1 online resource (xx, 136 pages) : color illustrations
Abstract
In recent years, aerial swarm technology has developed rapidly. In order to accomplish
a fully autonomous aerial swarm, a key technology is collaborative SLAM (CSLAM) for
aerial swarms, which estimates the relative pose and the consistent global trajectories.
In this thesis, we first propose a decentralized relative state estimation for aerial
swarms based on visual-inertial-UWB fusion to address the relative localization problem
of the aerial swarm. This algorithm is improved in subsequent studies, including
improving the initialization problem, introducing omnidirectional cameras to solve the
observability problem, and introducing map-based localization to feature global consistency.
We also investigate distributed SLAM for aerial swarms to solve the scaling issue.
BDPGO, a distribute...[
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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 collaborative SLAM (CSLAM) for
aerial swarms, which estimates the relative pose and the consistent global trajectories.
In this thesis, we first propose a decentralized relative state estimation for aerial
swarms based on visual-inertial-UWB fusion to address the relative localization problem
of the aerial swarm. This algorithm is improved in subsequent studies, including
improving the initialization problem, introducing omnidirectional cameras to solve the
observability problem, and introducing map-based localization to feature global consistency.
We also investigate distributed SLAM for aerial swarms to solve the scaling issue.
BDPGO, a distributed pose graph optimization (DPGO) algorithm that can schedule
computational resources on different unmanned aerial vehicles (UAV) to solve distributed
optimization in a balanced way, is proposed in this thesis. Our evaluation shows that
BDPGO improves the solving speed and reduces the communication overhead compared
to the previous DPGO methods.
Finally, based on the experience accumulated from our previous research, 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.
Our proposed methods successfully demonstrate centimeter-level accuracy on datasets
and extensive experiments. With the proposed methods, we have successfully conducted
extensive realistic experiments, including inter-UAV collision avoidance and multi-UAV
unknown environment exploration.
The techniques proposed in this thesis will contribute to the aerial swarm toward full
autonomy and efficiency.
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