視覚慣性センサデータを用いたUAVの自律航法と着陸のための姿勢推定学習
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data
視覚と慣性のマルチモーダル深層学習でUAVの姿勢を推定し、従来比25%の精度向上を達成。Airsimの飛行制御系に統合し自律航法・着陸をシミュレーションで実証した。
著者: Francesca Baldini, Animashree Anandkumar, Richard M. Murray
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
In this work, we propose a new learning approach for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). We develop a multimodal fusion of deep neural architectures for visual-inertial odometry. We train the model in an end-to-end fashion to estimate the current vehicle pose from streams of visual and inertial measurements. We first evaluate the accuracy of our estimation by comparing the prediction of the model to traditional algorithms on the publicly available EuRoC MAV dataset. The results illustrate a $25 \%$ improvement in estimation accuracy over the baseline. Finally, we integrate the architecture in the closed-loop flight control system of Airsim - a plugin simulator for Unreal Engine - and we provide simulation results for autonomous navigation and landing.
