Real-time Pose Estimation from Images for Multiple Humanoid Robots
This work addresses pose estimation for soccer-playing robots, enabling advanced behaviors, but it is incremental as it adapts existing models to a new domain.
The paper tackled real-time pose estimation for humanoid robots by proposing a lightweight model and introducing the HumanoidRobotPose dataset, achieving real-time performance in the RoboCup Humanoid League environment.
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.