首个面向工业场景椅子6D位姿估计的多传感器数据集
CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings
- 用三种RGBD传感器采集真实工业环境中的椅子数据
- 含77,811张带真值位姿的图像，每把椅子平均11,115个标注
- 适合研究机器人抓取与复杂遮挡下位姿估计的学者
在3D环境中准确估计复杂物体的6D位姿对有效机器人操作至关重要。然而，现有基准在评估6D位姿估计方法时仍存在局限，多数数据集聚焦于家庭环境中的家用物品，而少数工业数据集仅限于人工布置、放置于桌面的物体。为填补这一空白，我们提出CHIP，首个专为工业环境中由机械臂操作的椅子进行6D位姿估计设计的数据集。CHIP包含七种不同椅子，使用三种不同的RGBD传感技术采集，面临诸如具有细微差异的干扰物和由机械臂及操作员造成的严重遮挡等独特挑战。数据集共包含77,811张带真实6D位姿标注的RGBD图像，这些标注通过机器人运动学自动推导，平均每把椅子有11,115个标注。我们使用三种零样本6D位姿估计方法对CHIP进行了基准测试，评估了不同传感器类型、定位先验和遮挡水平下的性能表现。结果表明仍有巨大提升空间，凸显了该数据集的独特挑战性。CHIP将公开发布。
原文摘要 · Abstract (English)
Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall short in evaluating 6D pose estimation methods under realistic industrial conditions, as most datasets focus on household objects in domestic settings, while the few available industrial datasets are limited to artificial setups with objects placed on tables. To bridge this gap, we introduce CHIP, the first dataset designed for 6D pose estimation of chairs manipulated by a robotic arm in a real-world industrial environment. CHIP includes seven distinct chairs captured using three different RGBD sensing technologies and presents unique challenges, such as distractor objects with fine-grained differences and severe occlusions caused by the robotic arm and human operators. CHIP comprises 77,811 RGBD images annotated with ground-truth 6D poses automatically derived from the robot's kinematics, averaging 11,115 annotations per chair. We benchmark CHIP using three zero-shot 6D pose estimation methods, assessing performance across different sensor types, localization priors, and occlusion levels. Results show substantial room for improvement, highlighting the unique challenges posed by the dataset. CHIP will be publicly released.
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