ヒューマノイドロボット複数体の画像からのリアルタイム姿勢推定
Real-time Pose Estimation from Images for Multiple Humanoid Robots
RoboCupヒューマノイドリーグ向けに、複数のヒューマノイドロボットの姿勢を画像からリアルタイム推定する軽量モデルを提案し、新データセットHumanoidRobotPoseを構築した。
著者: Arash Amini, Hafez Farazi, Sven Behnke
分類: cs.RO, cs.CV
原文アブストラクト
Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos. With recent advancements in deep learning, we now have compelling models to tackle the problem in real-time. Since these models are usually designed for human images, one needs to adapt existing models to work on other creatures, including robots. This paper examines different state-of-the-art pose estimation models and proposes a lightweight model that can work in real-time on humanoid robots in the RoboCup Humanoid League environment. Additionally, we present a novel dataset called the HumanoidRobotPose dataset. The results of this work have the potential to enable many advanced behaviors for soccer-playing robots.
