ROS-based Multi-sensor Integrated Localization System for Cost-effective and Accurate Indoor Navigation System

Abstract

Accurate localization is essential for enabling intelligent autonomous navigation in indoor environments. While Global Navigation Satellite Systems (GNSS) provide efficient outdoor solutions, applications in indoor environments require alternative approaches in order to determine vehicle's global position. This study investigates a ROS-based multi-sensor integrated localization system utilizing wheel odometry, inertial measurement unit (IMU), and 2D Light Detection and Ranging (LiDAR) based simultaneous localization and mapping (SLAM) for cost-effective and accurate indoor autonomous vehicle (AV) navigation. The paper analyzes the limitations of wheel odometry and IMU, highlighting their susceptibility to errors. To address these limitations, the proposed system leverages LiDAR SLAM for real-time map generation and pose correction. The Karto SLAM package from robot operating system (ROS) is chosen due to its superior performance in previous research. Furthermore, the paper presents a low-cost prototype utilizing Raspberry Pi and Arduino Mega as the main controllers, demonstrating the feasibility of achieving accurate indoor localization with readily available hardware. This research contributes to the development of practical and cost-effective indoor navigation solutions for autonomous vehicles in wide range applications.