用视觉反馈控制机械臂，自动选择最佳拍摄角度提升产品姿态估计精度。
Object Pose Estimation by Camera Arm Control Based on the Next Viewpoint Estimation
- 基于深度网络联合预测视角与姿态，实现闭环优化。
- 姿态估计成功率77.3%，比传统方法高7.4个百分点。
- 适合零售场景中形状简单的产品自动识别与展示。
我们提出一种新方法，通过估计下一有效观测视角（Next Viewpoint, NV），提升零售店产品展示机器人对简单形状商品的姿态估计性能。基于RGBD相机的神经网络姿态估计方法虽精度高，但在当前视角纹理和形状特征不足时性能显著下降。而传统的数学模型方法难以针对此类物体生成有效视角。为此，我们关注姿态估计与视角预测之间的协同关系：姿态估计越准确，视角预测也越准确。因此，我们设计了一种新型神经网络，可同时输出姿态与最优视角。实验表明，该方法在姿态估计成功率达77.3%，较基于数学模型的视角计算方法高出7.4个百分点。此外，采用该方法的机器人成功展示了84.2%的产品。
原文摘要 · Abstract (English)
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.
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