Probabilistic Object Tracking using a Range Camera
This addresses object pose tracking for robotics and human-robot interaction, with incremental improvements by modeling occlusions explicitly.
The paper tackles the problem of tracking the 6-DoF pose of an object during manipulation by a human or robot, using a dynamic Bayesian network and a Rao-Blackwellised particle filter to handle occlusions, resulting in accurate and robust real-time tracking.
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.