论文标题
使用多视图优化的6D姿势估算RGB帧上无纹理对象的估计
6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization
论文作者
论文摘要
6D对无纹理对象的姿势估计是许多机器人应用的有价值但具有挑战性的任务。在这项工作中，我们提出了一个仅使用从多个角度获取的RGB图像来应对这一挑战的框架。我们方法的核心思想是将6D拟合估计为顺序的两步过程，首先估计3D翻译，然后估计每个对象的3D旋转。该解耦的配方首先解析了单个RGB图像中的规模和深度模棱两可，并使用这些估计值在第二阶段中准确识别对象方向，该对象方向通过准确的比例估算大大简化。此外，为了适应旋转空间中存在的多模式分布，我们开发了一种优化方案，该方案明确处理对象对称并抵消测量不确定性。与最先进的多视图方法相比，我们证明了所提出的方法可以在具有挑战性的6D姿势估计数据集的无纹理对象上取得了重大改进。
6D pose estimation of textureless objects is a valuable but challenging task for many robotic applications. In this work, we propose a framework to address this challenge using only RGB images acquired from multiple viewpoints. The core idea of our approach is to decouple 6D pose estimation into a sequential two-step process, first estimating the 3D translation and then the 3D rotation of each object. This decoupled formulation first resolves the scale and depth ambiguities in single RGB images, and uses these estimates to accurately identify the object orientation in the second stage, which is greatly simplified with an accurate scale estimate. Moreover, to accommodate the multi-modal distribution present in rotation space, we develop an optimization scheme that explicitly handles object symmetries and counteracts measurement uncertainties. In comparison to the state-of-the-art multi-view approach, we demonstrate that the proposed approach achieves substantial improvements on a challenging 6D pose estimation dataset for textureless objects.
