通过渲染对比分割结果，无需修改模型即可准确量化6D姿态估计的不确定性。
MaskVal: Simple but Effective Uncertainty Quantification for 6D Pose Estimation
- 用渲染的实例分割结果比对姿态估计，实现无需修改模型的不确定性评估。
- 在数据集和机器人场景中均显著优于现有集成方法，误差相关性更强。
- 适合对安全性要求高的机器人抓取等实际应用，提升操作可靠性。
在机器人应用中，可靠的6D姿态估计对于确保安全、可靠和可预测的操作性能至关重要。尽管如此，当前最先进的6D姿态估计算法通常不提供任何不确定性量化，或即便提供，其与真实误差的相关性也较弱。为此，本文提出一种简单而有效的不确定性量化方法MaskVal，该方法通过渲染并比较姿态估计与其对应的实例分割结果，无需修改原有姿态估计算法。尽管方法简洁，MaskVal在数据集和机器人实测场景中均显著优于现有的先进集成方法。实验表明，使用MaskVal可显著提升先进6D姿态估计算法的安全性和可靠性。此外，本文还提出一种针对机器人操作场景下6D姿态估计不确定性量化方法的新评估框架。
原文摘要 · Abstract (English)
For the use of 6D pose estimation in robotic applications, reliable poses are of utmost importance to ensure a safe, reliable and predictable operational performance. Despite these requirements, state-of-the-art 6D pose estimators often do not provide any uncertainty quantification for their pose estimates at all, or if they do, it has been shown that the uncertainty provided is only weakly correlated with the actual true error. To address this issue, we investigate a simple but effective uncertainty quantification, that we call MaskVal, which compares the pose estimates with their corresponding instance segmentations by rendering and does not require any modification of the pose estimator itself. Despite its simplicity, MaskVal significantly outperforms a state-of-the-art ensemble method on both a dataset and a robotic setup. We show that by using MaskVal, the performance of a state-of-the-art 6D pose estimator is significantly improved towards a safe and reliable operation. In addition, we propose a new and specific approach to compare and evaluate uncertainty quantification methods for 6D pose estimation in the context of robotic manipulation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。