Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many situations, and it also has the added benefits of lower system cost and energy footprint, which is especially important for micro aircraft. This paper proposes actively controlling and shaping the aircraft propulsion noise generated by the rotors to benefit localization tasks, rather than considering it a harmful nuisance. We present a neural network architecture for selfnoise-based localization in a known environment. We show that training it simultaneously with learning time-varying rotor phase modulation achieves accurate and robust localization. The proposed methods are evaluated using a computationally affordable simulation of MAV rotor noise in 2D acoustic environments that is fitted to real recordings of rotor pressure fields.

翻译：多旋翼自主飞行器主要依赖视觉进行导航。然而，视觉定位与里程计技术在低光照或直射阳光下表现不佳，视野有限且易受遮挡影响。声学感知在许多场景下可作为视觉的补充甚至替代模式，同时具有系统成本更低、能耗更小的附加优势，这对微型飞行器尤为重要。本文提出主动控制并整形旋翼产生的飞行器推进噪声以辅助定位任务，而非将其视为有害干扰。我们提出一种基于自噪声的已知环境定位神经网络架构。研究表明，将网络训练与学习时变旋翼相位调制同步进行，可实现精确且鲁棒的定位。该方法通过一种计算效率高的二维声学环境中多旋翼飞行器旋翼噪声仿真进行评估，该仿真基于实际旋翼压力场录音进行拟合。