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安防监控、地图测绘与物流配送等领域。在某些场景下，飞行器需在纯惯性导航模式下运行，导致导航解随时间发散。为解决此类问题并抑制导航漂移，四旋翼自主惯性推算(QDR)方法要求飞行器沿周期性轨迹飞行，进而可基于模型或学习方法实现位置矢量估计。本文提出采用多惯性测量单元(MIMU)提升QDR方法的定位精度，推导并评估了多种基于深度学习框架的MIMU数据融合方法。通过实地飞行实验验证了所提方法的有效性及其优势。