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Aerial vehicles are no longer limited to flying in open space: recent workhas focused on aerial manipulation and up-close inspection. Such applicationsplace stringent requirements on state estimation: the robot must combine stateinformation from many sources, including onboard odometry and globalpositioning sensors. However, flying close to or in contact with structures isa degenerate case for many sensing modalities, and the robot's state estimationframework must intelligently choose which sensors are currently trustworthy. Weevaluate a number of metrics to judge the reliability of sensing modalities ina multi-sensor fusion framework, then introduce a consensus-finding scheme thatuses this metric to choose which sensors to fuse or not to fuse. Finally, weshow that such a fusion framework is more robust and accurate than fusing allsensors all the time and demonstrate how such metrics can be informative inreal-world experiments in indoor-outdoor flight and bridge inspection.

To Fuse or Not to Fuse: Measuring Consistency in Multi-Sensor Fusion for Aerial Robots