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.

翻译：空中飞行器不再局限于在开阔空间中运行：近期研究聚焦于空中操作与近距离检测。这类应用对状态估计提出了严格的要求：机器人必须融合来自多个来源的状态信息，包括机载里程计与全球定位传感器。然而，在接近或接触结构物飞行时，许多传感模态都将面临退化情况，因此机器人的状态估计框架必须智能选择当前可信的传感器。我们评估了多传感器融合框架中用于判断传感模态可靠性的多项指标，随后提出了一种共识发现方案，该方案利用此指标决定融合或不融合哪些传感器。最后，我们证明这种融合框架比始终融合所有传感器的方法更鲁棒、更精确，并通过室内外飞行及桥梁检测的真实实验展示了此类指标的有效性。