Implementation of Mobile Sensor Navigation System Based on Adaptive Monte Carlo Localization

Abstract

Adaptive Monte Carlo Localization is a method used for mobile sensor localization in environment with representations of particle filters and Kullback-Leibler Distance (KLD) sampling to accelerate time execution of Localization. Mobile sensor has the ability to explore previously unknown environments using the mapping method. The mobile sensor must localize the pose (position and orientation) inside the operating environment before navigating. The final step is to navigate automatically to the specific point in the by using Cartesian Coordinate 2-dimensions (x,y). In this paper concerned about this AMCL algorithm in Robot Operating System (ROS), by using the different number of particle that is used for localization of the actual robot position and used it for navigating. The experiments that have been done, a map is obtained and can do the Localization process with Adaptive Monte Carlo Localization and the accuracy of the navigation process is influenced by the number of particles and the surrounding environment.