Adaptive Monte Carlo Localization for Mobile Robots

Local and global localization are of utmost importance in mobile robotics and play a crucial role in a robot going from remote-controlled to completely autonomous. A probabilistic localization algorithm known as the Adaptive Monte Carlo Localization (AMCL) uses a set of weighted particles to approximate the position and orientation of a robot. In this experiment, this algorithm is used to localize a simulated mobile robot in the Gazebo environment. After deploying the AMCL and tuning its parameters to a specific mobile robot, the particles quickly converge on the pose. These parameters were then tested on a similar robot in the same environment and a similar convergence resulted. These results show that the AMCL is a viable localization algorithim and performs well in these conditions.