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.

翻译：单视角RGB物体姿态估计器已达到较高的精度与效率，使其成为基于视觉的机器人控制的优质候选方案。然而，现成方法缺乏时域一致性和鲁棒性，而这两点对于稳定的反馈控制至关重要。本文提出一种因子图方法，以强制实现物体姿态估计的时域一致性。具体而言，所提方法：(i) 融合物体运动模型，(ii) 显式估计物体姿态测量不确定性，(iii) 将上述两个组件整合至基于在线优化的估计器中。我们证明，通过使用所提因子图方法进行适当的离群值剔除与平滑处理，能够在标准化姿态估计基准测试中显著提升结果。通过实验验证了所提方法在基于反馈的机器人控制任务中的稳定性，该任务中物体由安装在力矩控制型机械臂上的摄像头进行跟踪。