Self-adaptive Monte Carlo method for indoor localization of smart AGVs using LIDAR data

The vehicles used for transportation and logistics in the factories usually perceive their surroundings with range sensors. Today, 2D LIDARs are used as range sensors, and 3D LIDARs are becoming widespread with the developments of autonomous vehicle technology. Therefore, Self Adaptive Monte Carlo Localization, abbreviated as SA-MCL, is improved in this study to make the algorithm suitable for autonomous guided vehicles (AGVs) equipped with 2D or 3D LIDARs. Moreover, the traditional SA-MCL algorithm has a constraint that the range sensors on the robot are uniformly placed, and ellipse based energy model is proposed in this study to remove the constraint. This model can compute the energy value regardless of the robot orientation since it considers offsets due to the asymmetric placement of range sensors on the robot. The importance of localization increases since it is aimed that AGVs to be used in smart factories are able to use entire free space on the map in order to provide energy efficiency and time saving, and perform tasks that can vary at anytime instead of routine. SA-MCL algorithm is preferred in this study since traditional SA-MCL can overcome global localization, position tracking and kidnapping sub-problems of localization. The algorithm proposed in this study is verified to demonstrate its performance and effectiveness both in simulation and experimental studies using MATLAB and robot operating system (ROS).