Parameter Tuning of G-mapping SLAM (Simultaneous Localization and Mapping) on Mobile Robot with Laser-Range Finder 360° Sensor

Abstract

The development of research and mapping technology based on automatic navigation directly by utilizing the SLAM or Simultaneous Localization and Mapping algorithm is increasingly widespread. One algorithm that works well on navigation sensors, specifically the Laser-Range Finder 3600 sensor, is G-mapping SLAM. GAM mapping SLAM works by utilizing the Rao-Blackwellized Particle Filter that has been developed to build mapping based on Occupancy Grid. The purpose of this research was to tune the parameters of the SLAM G-mapping algorithm itself to produce an accurate room mapping where the mapping results will be used for automatic navigation purposes. The result of this research was that the required particle value was at least 5, the Resampling Threshold parameter was at least between 0.5 and also gradually reduced the parameter values of the Linear step update and Angular step update to produce a good mapping and also reduced the uncertainty value of the robot pose. When tested into autonomous navigation stack in the robot, it was capable of navigating from home room to the navigation goal within 25 seconds.