## page was renamed from world_modeling
<<StackHeader(wire)>>

== Introduction ==
Wire generates and maintains one consistent world state estimate based on object detections. It solves the data association problem by maintaining multiple hypotheses and facilitates tracking of various object attributes. The state estimators used for estimation and the probabilistic models used for association can be configured. Technical details can be found in this paper:

J. Elfring, S. van den Dries, M.J.G. van de Molengraft, M. Steinbuch, Semantic world modeling using probabilistic multiple hypothesis anchoring, Robotics and Autonomous Systems, Volume 61, Issue 2, February 2013, Pages 95-105, ([[http://dx.doi.org/10.1016/j.robot.2012.11.005|pdf]])

which also includes a more detailed explanation of the algorithm

<<Youtube(L38yWVEr9DA&rel=0)>>

== Installation ==
To install the ''wire'' software, clone the source into your workspace:

{{{
git clone https://github.com/tue-robotics/wire.git
}}}
Then compile the workspace:

{{{
catkin_make
}}}
== Tutorials ==
We have created a set of [[wire/Tutorials|tutorials]] explaining how to use, tune and interpret the world model and the resulting world state estimate.

=== Beginner tutorials: demos and visualization ===
<<TutorialChain(wire/Tutorials/Fusing multiple measurements originating from the same object)>>

=== Intermediate tutorials: using the world model ===
<<TutorialChain(wire/Tutorials/Feeding detections to the world model)>>

=== Advanced tutorials: tuning the world model ===
<<TutorialChain(wire/Tutorials/Tuning the world model: new objects)>>

== Report a Bug ==
<<TracLink(REPO COMPONENT)>>

##i.e.<<TracLink(ros-pkg drivers)>>
## AUTOGENERATED DON'T DELETE
## CategoryStack