ROS-based multi-sensor integrated localization system for cost-effective and accurate indoor navigation system

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 to determine the 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 according to the literature. Results indicate that the integration of these technologies reduces localization errors significantly, with the system achieving a high degree of accuracy in pose estimation under various test conditions. The experimental validation shows that the proposed system maintains consistent performance, proving its potential for widespread application in environments where GNSS is unavailable.