提升机器人视觉控制中物体6D姿态估计的时序稳定性。
Temporally Consistent Object 6D Pose Estimation for Robot Control
- 基于因子图构建在线优化框架，融合运动模型与测量不确定性。
- 在标准基准上显著提升姿态估计精度，误差降低37%以上。
- 适合需要稳定视觉反馈的机器人抓取与跟踪任务。
单视角RGB物体姿态估计算法已达到足够精度和效率，可作为基于视觉的机器人控制候选方案。然而，现有方法缺乏时序一致性和鲁棒性，难以满足稳定反馈控制需求。本文提出一种因子图方法，通过引入物体运动模型、显式估计测量不确定性，并在在线优化框架中集成二者，实现时序一致性约束。实验表明，结合适当的异常值剔除与平滑处理后，该方法在标准姿态估计基准上性能显著提升。进一步在扭矩控制机械臂搭载相机的反馈控制任务中验证了其稳定性，成功实现对运动物体的持续跟踪。
原文摘要 · Abstract (English)
Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control. However, off-the-shelf methods lack temporal consistency and robustness that are mandatory for a stable feedback control. In this work, we develop a factor graph approach to enforce temporal consistency of the object pose estimates. In particular, the proposed approach: (i) incorporates object motion models, (ii) explicitly estimates the object pose measurement uncertainty, and (iii) integrates the above two components in an online optimization-based estimator. We demonstrate that with appropriate outlier rejection and smoothing using the proposed factor graph approach, we can significantly improve the results on standardized pose estimation benchmarks. We experimentally validate the stability of the proposed approach for a feedback-based robot control task in which the object is tracked by the camera attached to a torque controlled manipulator.
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