Development of Mapping and Localization System for A Three Wheel Omniwheel Robot using Odometry and G-Mapping

Abstract

Accurate mapping and localization are essential for autonomous robot navigation. However, odometry-based localization often suffers from cumulative errors that cause positional drift. This study integrates odometry with LiDAR-based Simultaneous Localization and Mapping (SLAM) using the G-Mapping algorithm on a three-wheeled omniwheel robot within the Robot Operating System (ROS) framework.Experiments conducted in a 148 × 199 cm indoor arena achieved a mapping accuracy of 96.87%, showing significant improvement over odometry-only methods and outperforming comparable Turtlebot-based SLAM systems. The generated 2D occupancy maps closely matched the actual environment, maintaining stable localization even during repeated trials.Some limitations were observed, such as small objects undetected by LiDAR and processing constraints from the Jetson Nano 2GB. Future work will focus on testing in larger, more dynamic environments and optimizing processing performance. The novelty of this research lies in integrating G-Mapping with a compact omniwheel platform, enhancing mapping precision, maneuverability, and real-time localization for indoor robotics.