距離カメラを用いた確率的物体追跡
Probabilistic Object Tracking using a Range Camera
距離カメラの観測から、人やロボットが操作中の物体の6自由度姿勢をベイズフィルタでリアルタイムに推定する手法を提案した論文。自己遮蔽や環境による遮蔽を明示的にモデル化している。
著者: Manuel Wüthrich, Peter Pastor, Mrinal Kalakrishnan, Jeannette Bohg, Stefan Schaal
分類: cs.RO
原文アブストラクト
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.
