Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Object Tracking using a Range Camera   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.

20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn