We present a dead reckoning strategy for increased resilience to position estimation failures on multirotors, using only data from a low-cost IMU and novel, bio-inspired airflow sensors. The goal is challenging, since low-cost IMUs are subject to large noise and drift, while 3D airflow sensing is made difficult by the interference caused by the propellers and by the wind. Our approach relies on a deep-learning strategy to interpret the measurements of the bio-inspired sensors, a map of the wind speed to compensate for position-dependent wind, and a filter to fuse the information and generate a pose and velocity estimate. Our results show that the approach reduces the drift with respect to IMU-only dead reckoning by up to an order of magnitude over 30 seconds after a position sensor failure in non-windy environments, and it can compensate for the challenging effects of turbulent, and spatially varying wind.

翻译：我们提出了一个提高多色器估计失败的适应力的死算战略,仅使用低成本的IMU和新颖的、受生物启发的空气流传感器的数据。 目标具有挑战性,因为低成本的IMU受到大噪音和漂移的影响,而3D空气流感则由于螺旋桨和风造成的干扰而变得困难。 我们的方法依靠深层学习战略来解释生物感应器的测量结果,一份风速图来补偿依赖位置的风,以及一个过滤器来连接信息并产生一个成形和速度的估测。 我们的结果显示,这种方法可以减少仅由IMU在非风环境中定位传感器失灵后30秒内进行大量计数的流动,并可以弥补动荡和空间变化的风造成的具有挑战性的影响。