融合还是不融合：空中机器人多传感器融合的一致性度量
To Fuse or Not to Fuse: Measuring Consistency in Multi-Sensor Fusion for Aerial Robots
AI总结：
针对空中机器人在结构附近飞行时多传感器融合的退化问题，提出基于可靠性度量的共识发现方案，动态选择融合传感器，实验证明比始终融合所有传感器更鲁棒准确。
摘要：
空中飞行器不再局限于在开阔空间中飞行：近期的工作聚焦于空中操纵和近距离检查。这类应用对状态估计提出了严格要求：机器人必须结合来自多个来源的状态信息，包括机载里程计和全球定位传感器。然而，在结构附近飞行或与之接触时，许多传感模态会退化为退化情形，机器人的状态估计框架必须智能地选择当前可信的传感器。我们在多传感器融合框架中评估了多种度量指标，以判断传感模态的可靠性，然后引入一种共识发现方案，利用该度量选择融合或不融合哪些传感器。最后，我们表明这种融合框架比始终融合所有传感器更鲁棒、更准确，并展示了这些度量如何在室内外飞行和桥梁检查的真实世界实验中提供信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
