最適制御と学習を組み合わせた未知屋内環境での自律飛行ナビゲーション
Combining optimal control and learning for autonomous aerial navigation in novel indoor environments
シミュレータの特権情報から最適な3D経路点を生成し、RGBとIMUのみから同様の障害物回避経路点を出力する知覚モジュールを学習させ、iGibson環境で有効性を示した。
著者: Kevin Lin, Brian Huo, Megan Hu
分類: cs.RO
原文アブストラクト
This report proposes a combined optimal control and perception framework for Micro Aerial Vehicle (MAV) autonomous navigation in novel indoor enclosed environments, relying exclusively on on-board sensor data. We use privileged information from a simulator to generate optimal waypoints in 3D space that our perception system learns to imitate. The trained learning based perception module in turn is able to generate similar obstacle avoiding waypoints from sensor data (RGB + IMU) alone. We demonstrate the efficacy of the framework across novel scenes in the iGibson simulation environment.
