Outdoor Positioning Based on ROS LiDAR Navigation Compared with RTK GPS Accuracy

Abstract

This study proposes the ROS 1 framework and a 2D LiDAR employed two algorithms based on particle filtering for SLAM (Simultaneous Localization and Mapping). The algorithms include gmapping (Grid-based FastSLAM Mapping) and adaptive Monte Carlo localization (AMCL) to establish maps and determine the positions for the robot navigation. Gmapping disperses particles generated by the 2D LiDAR throughout the environment, calculates particle weights, and then constructs a map by drawing these particles. AMCL matches them with known maps to estimate the robot's position. The navigation is carried out using the move_base for the analysis of six outdoor positioning points of the 2D LiDAR navigation robot, and the comparison uses RTK-GPS as the reference point. The target aims to measure the errors of the 2D LiDAR navigation robot concerning these six points. The comparison shows that the root mean square (RMS) errors of RTK-GPS are consistently smaller than those of 2D LiDAR.