This work presents a number of techniques to improve the ability to create magnetic field maps on a UAV which can be used to quickly and reliably gather magnetic field observations at multiple altitudes in a workspace. Unfortunately, the electronics on the UAV can introduce their own magnetic fields, distorting the resultant magnetic field map. We show methods of reducing and working with UAV-induced noise to better enable magnetic fields as a sensing modality for indoor navigation. First, some gains in our flight controller create high-frequency motor commands that introduce large noise in the measured magnetic field. Next, we implement a common noise reduction method of distancing the magnetometer from other components on our UAV. Finally, we introduce what we call a compromise GPR (Gaussian process regression) map that can be trained on multiple flight tests to learn any flight-by-flight variations between UAV observation tests. We investigate the spatial density of observations used to train a GPR map then use the compromise map to define a consistency test that can indicate whether or not the magnetometer data and corresponding GPR map are appropriate to use for state estimation. The interventions we introduce in this work facilitate indoor position localization of a UAV whose estimates we found to be quite sensitive to noise generated by the UAV.

翻译：本文提出一系列技术，旨在提升无人机（UAV）磁场地图的创建能力，使其能快速可靠地采集工作空间内多个高度的磁场观测数据。然而，无人机电子设备自身产生的磁场会干扰最终磁场地图的准确性。我们展示了减少并应对无人机诱发噪声的方法，以更好地利用磁场作为室内导航的传感模态。首先，飞行控制器中的某些增益会产生高频电机指令，导致测量的磁场出现大幅噪声。其次，我们实施了将磁力计与无人机其他组件隔离的常见降噪方法。最后，我们引入一种称为妥协GPR（高斯过程回归）地图的技术，该地图可通过多次飞行测试训练，学习不同飞行观测测试间的飞行间差异。我们研究了用于训练GPR地图的观测空间密度，并利用妥协地图定义一致性检验，以判断磁力计数据及对应的GPR地图是否适用于状态估计。本文引入的干预措施促进了无人机室内位置定位，我们已发现其估计对无人机自身产生的噪声极为敏感。