Probabilistic object tracking using a range camera
Probabilistic object tracking using a range camera
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DOI：
10.1109/iros.2013.6696810
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Manuel Wüthrich;P. Pastor;Mrinal Kalakrishnan;J. Bohg;S. Schaal
中科院分区：
文献类型：
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作者：
Manuel Wüthrich;P. Pastor;Mrinal Kalakrishnan;J. Bohg;S. Schaal
我们解决的问题，跟踪的6自由度构成的对象，而它是由一个人或机器人操纵。我们使用一个动态贝叶斯网络进行推理，并计算后验分布在当前的对象姿势。根据机器人或人类是否操纵对象，我们采用一个过程模型，或不知道控制输入。观测是从一台测距照相机上获得的。相对于以前的对象跟踪方法，我们明确地建模自遮挡和遮挡的环境，例如，人类或机器人的手。这导致了强非线性观测模型和贝叶斯网络中的额外依赖性。我们使用Rao-Blackwellised粒子滤波器来计算每个时间步的对象姿态估计。在一组实验中，我们证明了我们的方法能够在人类或机器人操纵物体时实时准确且鲁棒地跟踪物体姿态。
We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.