仅用3D点云估计物体姿态分布，解决工业场景下无颜色信息的不确定性问题
Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds
- 基于深度神经网络，仅使用3D点云估计姿态分布，无需颜色信息
- 在真实抓取场景中验证，对反射和旋转对称性带来的不确定性有效建模
- 适用于工业机器人感知，尤其适合无彩色数据的环境
物体姿态估计对机器人感知至关重要，传统方法通常只输出单一姿态，无法捕捉由视觉模糊引起的不确定性，可能导致不可靠行为。现有姿态分布方法高度依赖颜色信息，在工业场景中常不可用。本文提出一种新型神经网络方法，仅利用3D无色点云估计姿态不确定性。据我们所知，这是首个不依赖RGB输入即可进行深度学习姿态分布估计的方法。我们在具有不同几何模糊性的物体真实抓取场景中验证了该方法的有效性。当前实现聚焦于反射与旋转对称性引起的不确定性，但框架可扩展至完整的SE(3)姿态分布估计。源代码见opde3d.github.io。
原文摘要 · Abstract (English)
Object pose estimation is crucial to robotic perception and typically provides a single-pose estimate. However, a single estimate cannot capture pose uncertainty deriving from visual ambiguity, which can lead to unreliable behavior. Existing pose distribution methods rely heavily on color information, often unavailable in industrial settings. We propose a novel neural network-based method for estimating object pose uncertainty using only 3D colorless data. To the best of our knowledge, this is the first approach that leverages deep learning for pose distribution estimation without relying on RGB input. We validate our method in a real-world bin picking scenario with objects of varying geometric ambiguity. Our current implementation focuses on symmetries in reflection and revolution, but the framework is extendable to full SE(3) pose distribution estimation. Source code available at opde3d.github.io
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