We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured models of these objects, suitable for generating synthetic training data, as well as RGBD images of the objects in challenging, cluttered scenes exhibiting partial occlusion, extreme lighting variations, multiple instances per image, and a large variety of poses. Using semi-automated RGBD-to-model texture correspondences, the images are annotated with ground truth poses accurate within a few millimeters. We also propose a new pose evaluation metric called ADD-H based on the Hungarian assignment algorithm that is robust to symmetries in object geometry without requiring their explicit enumeration. We share pre-trained pose estimators for all the toy grocery objects, along with their baseline performance on both validation and test sets. We offer this dataset to the community to help connect the efforts of computer vision researchers with the needs of roboticists.
翻译:我们为6-DoF提供了一套新数据集,用于估计已知的物体,重点是机器人操纵研究。我们提出一套玩具杂货物品,这些物品的物理即时性随时可以购买,并且适合机器人的捕捉和操纵。我们提供了这些物品的3D扫描纹理模型,适合于生成合成培训数据,以及具有挑战性的、杂乱无章的物体图像RGBD,显示部分封闭、极端照明变异、每个图像多例和各种外形。我们使用半自动RGBD到模型的纹理通信,这些附有地面真象的附加说明的图像在几毫米内就准确无误了。我们还根据匈牙利的外派算法提出了称为ADD-H的新的姿势评价指标,该算法在不需明确查点的情况下对物体的几度进行对比。我们分享了所有微小的杂货物品的预先训练姿势估测图,同时在验证和测试机组上都有基线性表现。我们向社区提供这一数据集,以帮助计算机视觉研究人员的努力与机器人的需要联系起来。