Object Pose Estimation by Camera Arm Control Based on the Next Viewpoint Estimation
This work addresses the challenge of improving robot product display efficiency in retail stores, representing an incremental advance in domain-specific robotics.
The paper tackles the problem of accurate pose estimation for simple-shaped objects in retail display robots by developing a neural network that simultaneously estimates object pose and the next viewpoint, achieving a 77.3% success rate in pose estimation, which is 7.4 percentage points higher than previous mathematical model-based methods.
We have developed a new method to estimate a Next Viewpoint (NV) which is effective for pose estimation of simple-shaped products for product display robots in retail stores. Pose estimation methods using Neural Networks (NN) based on an RGBD camera are highly accurate, but their accuracy significantly decreases when the camera acquires few texture and shape features at a current view point. However, it is difficult for previous mathematical model-based methods to estimate effective NV which is because the simple shaped objects have few shape features. Therefore, we focus on the relationship between the pose estimation and NV estimation. When the pose estimation is more accurate, the NV estimation is more accurate. Therefore, we develop a new pose estimation NN that estimates NV simultaneously. Experimental results showed that our NV estimation realized a pose estimation success rate 77.3\%, which was 7.4pt higher than the mathematical model-based NV calculation did. Moreover, we verified that the robot using our method displayed 84.2\% of products.