  We propose a system solution to achieve data-efficient, decentralized state
estimation for a team of flying robots using thermal images and inertial
measurements. Each robot can fly independently, and exchange data when possible
to refine its state estimate. Our system front-end applies an online
photometric calibration to refine the thermal images so as to enhance feature
tracking and place recognition. Our system back-end uses a
covariance-intersection fusion strategy to neglect the cross-correlation
between agents so as to lower memory usage and computational cost. The
communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to
construct a request-response policy that requires low bandwidth usage. We test
our collaborative method on both synthetic and real-world data. Our results
show that the proposed method improves by up to 46 % trajectory estimation with
respect to an individual-agent approach, while reducing up to 89 % the
communication exchange. Datasets and code are released to the public, extending
the already-public JPL xVIO library.
