日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

単眼ナビゲーションarXiv:1604.04779

障害物の多い屋外環境における堅牢な単眼飛行

Robust Monocular Flight in Cluttered Outdoor Environments

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小型ドローンがカメラのみで森林などの複雑な屋外環境を自律飛行できるよう、リアルタイムの半密な深度推定と耐風制御を組み合わせたシステムを開発し、実環境で実証した。

著者: Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew Bagnell, Martial Hebert

分類: cs.RO

原文アブストラクト

Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straight-forward for large UAVs as expensive sensors and monitoring devices can be employed, robust methods for obstacle avoidance remains a challenging task for MAVs which operate at low altitude in cluttered unstructured environments. Due to payload and power constraints, it is necessary for such systems to have autonomous navigation and flight capabilities using mostly passive sensors such as cameras. In this paper, we describe a robust system that enables autonomous navigation of small agile quad-rotors at low altitude through natural forest environments. We present a direct depth estimation approach that is capable of producing accurate, semi-dense depth-maps in real time. Furthermore, a novel wind-resistant control scheme is presented that enables stable way-point tracking even in the presence of strong winds. We demonstrate the performance of our system through extensive experiments on real images and field tests in a cluttered outdoor environment.

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