Probabilistic Model-Based Global Localization in an Airport Environment

This paper presents a method for 3D robot localization in an airport environment using an enhanced adaptive Monte Carlo localization algorithm with sensor input from a multi-beam lidar. The occupancy grid is computed based on the known geometry of various airplane types. By inserting adaptive particle noise, an estimated global pose can be obtained reliably in stationary conditions with a low number of initial particles. As the probabilistic particle filter converges, the particle noise and the number of particles are reduced. Robot odometry is used to propagate the candidate particles when moving. The algorithm has been implemented within the Robot Operating System (ROS) framework and can run in real-time on a low-power computing device on the robot. Comparison of the numerous enhancements are shown in simulation. The results have been validated in practice on multiple airplanes at two airports showing good performance.