Error-State Unscented Kalman-Filter for UAV Indoor Navigation

In this paper, we present an algorithm for indoor quadcopter navigation. We implemented a strapdown navigation algorithm combined with an error-state unscented Kalman-Filter capable of fusing IMU, barometer and UWB measurements. Optical flow and distance to ground measurements are additionally fused to further improve the state estimation quality. Compared to alternate approaches, the suggested algorithm has better trajectory following abilities and does not rely on the actual quadcopter's dynamics, so it can be applied to a variety of flying platforms. We implemented and evaluated the algorithm on the Crazyflie 2.1 nano-quadcopter.