マルチロータ向け気流慣性オドメトリによる堅牢な状態推定
Airflow-Inertial Odometry for Resilient State Estimation on Multirotors
低コストIMUと生物模倣の気流センサのみを用い、深層学習と風速マップ、フィルタ融合で位置センサ故障時にもドリフトを大幅に抑えるデッドレコニング手法を提案した。
著者: Andrea Tagliabue, Jonathan P. How
分類: cs.RO
原文アブストラクト
We present a dead reckoning strategy for increased resilience to position estimation failures on multirotors, using only data from a low-cost IMU and novel, bio-inspired airflow sensors. The goal is challenging, since low-cost IMUs are subject to large noise and drift, while 3D airflow sensing is made difficult by the interference caused by the propellers and by the wind. Our approach relies on a deep-learning strategy to interpret the measurements of the bio-inspired sensors, a map of the wind speed to compensate for position-dependent wind, and a filter to fuse the information and generate a pose and velocity estimate. Our results show that the approach reduces the drift with respect to IMU-only dead reckoning by up to an order of magnitude over 30 seconds after a position sensor failure in non-windy environments, and it can compensate for the challenging effects of turbulent, and spatially varying wind.
