  Aerial vehicles are no longer limited to flying in open space: recent work
has focused on aerial manipulation and up-close inspection. Such applications
place stringent requirements on state estimation: the robot must combine state
information from many sources, including onboard odometry and global
positioning sensors. However, flying close to or in contact with structures is
a degenerate case for many sensing modalities, and the robot's state estimation
framework must intelligently choose which sensors are currently trustworthy. We
evaluate a number of metrics to judge the reliability of sensing modalities in
a multi-sensor fusion framework, then introduce a consensus-finding scheme that
uses this metric to choose which sensors to fuse or not to fuse. Finally, we
show that such a fusion framework is more robust and accurate than fusing all
sensors all the time and demonstrate how such metrics can be informative in
real-world experiments in indoor-outdoor flight and bridge inspection.
