论文标题
基于物理的对象6D置率估计在非划出操纵期间
Physics-Based Object 6D-Pose Estimation during Non-Prehensile Manipulation
论文作者
论文摘要
我们提出了一种随着时间的时间跟踪对象的6D姿势的方法，而对象则由机器人进行非划和操纵。在操纵对象的任何给定时间，我们假设可以访问机器人关节控件和相机的图像。我们使用机器人关节控件来执行基于物理的预测对象的移动方式。然后，我们将该预测与来自相机的观察结果相结合，以尽可能准确地估算物体姿势。我们使用粒子过滤方法将控制信息与视觉信息相结合。我们将提出的方法与两个基准进行比较：（i）在每个时间步长仅使用基于图像的姿势估计系统，以及（ii）不执行计算昂贵的物理预测的粒子滤波器，但假设对象以恒定速度移动。我们的结果表明，制作基于物理的预测值得计算成本，从而更准确地跟踪，并估算对象姿势，即使相机不清楚地看到对象。
We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.
