Quadrotor Ego-Noise-Based Passive Acoustic Sensing for Obstacle Detection

Sound carries rich information, yet its utilization in quadrotor-based applications, particularly in extracting environmental information from ego-noise, has received limited attention. This paper researches the interaction between quadrotor ego-noise and its corresponding echoes, exploiting phase cancellation effects to detect obstacles. A waveform detection algorithm integrating the Root Mean Square (RMS) of sound energy has been developed for obstacle detection. The performance indicates that using acoustic information in autonomous robotics for obstacle detection is feasible, especially in high-noise mobile platforms such as quadrotors. Future research will focus on increasing detection accuracy and enabling precise obstacle distance estimation.