Sound-based Distance Estimation for Indoor Navigation in the Presence of Ego Noise

An off-the-shelf drone for indoor operation would come with a variety of different sensors that are used concurrently to avoid collision with, e.g., walls, but these sensors are typically uni-directional and offers limited spatial awareness. In this paper, we propose a model-based technique for distance estimation using sound and its reflections. More specifically, the technique is estimating Time-of-Arrivals (TOAs) of the reflected sound that could infer knowledge about room geometry and help in the design of sound-based collision avoidance. Our proposed solution is thus based on probing a known sound into an environment and then estimating the TOAs of reflected sounds recorded by a single microphone. The simulated results show that our approach to estimating TOAs for reflector position estimation works up to a distance of at least 2 meters even with significant additive noise, e.g., drone ego noise.