A fast particle filter implementation and sensor models for localizing a robot which always take into account the most recent state of the world. This means that if the world representation improves while the robot is running, localization becomes better. The localization module is more efficient and accurate than the well-known AMCL-module and no separate occupancy grid is needed.

Requirements:

• ED (https://github.com/tue-robotics/ed.git)
• A 2D Range Finder (http://wiki.ros.org/Sensors) which scans in a plane parallel to the floor
• A TF containing transforms from the robots' odometry frame to the laser range finder frame

Check out the following packages in your workspace:

cd <your_catkin_workspace>/src
git clone https://github.com/tue-robotics/ed_localization.git

And compile

cd <your_catkin_workspace>:
catkin build

All ED tutorials can be found in the ed_tutorials package: https://github.com/tue-robotics/ed_tutorials.git