Quadrotors are widely used for surveillance, mapping, and deliveries. In several scenarios the quadrotor operates in pure inertial navigation mode resulting in a navigation solution drift. To handle such situations and bind the navigation drift, the quadrotor dead reckoning (QDR) approach requires flying the quadrotor in a periodic trajectory. Then, using model or learning based approaches the quadrotor position vector can be estimated. We propose to use multiple inertial measurement units (MIMU) to improve the positioning accuracy of the QDR approach. Several methods to utilize MIMU data in a deep learning framework are derived and evaluated. Field experiments were conducted to validate the proposed approach and show its benefits.

翻译：四旋翼飞行器广泛应用于监视、测绘和货物投递。在某些场景下，四旋翼飞行器以纯惯性导航模式运行，导致导航解出现漂移。为应对此类情况并抑制导航漂移，四旋翼航位推算方法要求飞行器沿周期性轨迹飞行，进而通过基于模型或学习的方法估算飞行器的位置矢量。本文提出采用多惯性测量单元以提升航位推算方法的定位精度，推导并评估了在深度学习框架下利用多惯性测量单元数据的多种方法，通过实地实验验证了所提方法的有效性并展示了其优势。