マルチロータ機の位置推定のための能動的な推進騒音整形
Active propulsion noise shaping for multi-rotor aircraft localization
ロータの推進騒音を制御・整形して位置推定に活用する手法を提案し、既知環境での自己騒音ベース位置推定のニューラルネットを時間変化するロータ位相変調と同時学習することで高精度かつロバストな推定を実現した。
著者: Gabriele Serussi, Tamir Shor, Tom Hirshberg, Chaim Baskin, Alex Bronstein
分類: cs.RO, cs.AI
原文アブストラクト
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.
