DRONAR: Obstacle echolocation using ego-noise

Abstract: You do not want your drone to crash. Therefore, safety systems should be put in place to prevent such an event, and obstacle avoidance is a major part of this. Today, the most successful techniques use cameras or light detection and ranging (LIDAR) to find and avoid obstacles; but to improve resiliency, multiple systems should be used. This thesis proposes to use microphones, listening to the drone’s own noise, to estimate the distance to surrounding obstacles. An obstacle echolocation solution for multi-rotor aerial vehicles (MAVs) using ego-noise is developed. The MAV’s noise is captured and auto-correlated to detect echoes at different time delays. This signal is whitened to remove structured measurement noise resulting from the narrow-band components of the MAV’s noise. By recording the MAV’s noise using multiple microphones, a time of arrival (TOA) estimate of the obstacle position is achieved. A beamforming-based solution is used to calculate this estimate. A series of simplified proof-of-concept experiments show that ego-noise echolocation is possible and that the developed solution works in a controlled environment. A prototype implementation of a realistic system is also created. Four signal fusion alternatives are compared, though no best alternative is found for all situations. More work is needed to apply the findings of this work in a robust way, but the principle is shown to work.