ビデオを用いた自律飛行体のリアルタイムナビゲーション
Real-Time Navigation for Autonomous Aerial Vehicles Using Video
ドローンなどの自律飛行体がカメラ映像から意味情報を抽出してナビゲーションする際の計算負荷を、マルコフ決定過程フレームワークで軽減する手法を提案。
著者: Khizar Anjum, Parul Pandey, Vidyasagar Sadhu, Roberto Tron, Dario Pompili
分類: cs.RO, cs.CV, eess.IV
原文アブストラクト
Most applications in autonomous navigation using mounted cameras rely on the construction and processing of geometric 3D point clouds, which is an expensive process. However, there is another simpler way to make a space navigable quickly: to use semantic information (e.g., traffic signs) to guide the agent. However, detecting and acting on semantic information involves Computer Vision~(CV) algorithms such as object detection, which themselves are demanding for agents such as aerial drones with limited onboard resources. To solve this problem, we introduce a novel Markov Decision Process~(MDP) framework to reduce the workload of these CV approaches. We apply our proposed framework to both feature-based and neural-network-based object-detection tasks, using open-loop and closed-loop simulations as well as hardware-in-the-loop emulations. These holistic tests show significant benefits in energy consumption and speed with only a limited loss in accuracy compared to models based on static features and neural networks.
