In this chapter, we are using the Adaptive Monte Carlo Localization (AMCL) algorithm for the localization. The AMCL algorithm is a probabilistic localization system for a robot moving in 2D. This system implements the adaptive Monte Carlo Localization approach, which uses a particle filter to track the pose of a robot against a known map.
The AMCL algorithm has many configuration options that will affect the performance of localization. For more information on AMCL, please refer to the AMCL documentation at http://wiki.ros.org/amcl and also at http://www.probabilistic-robotics.org/.
The amcl node works mainly with laser scans and laser maps, but it could be extended to work with other sensor data, such as a sonar or stereo vision. So for this chapter, it takes a laser-based map and laser scans, transforms messages, and generates a probabilistic pose. On startup, the amcl node initializes its particle filter according to the parameters provided in the setup...