Development of a Low-Cost SLAM System for Autonomous Mapping of Complex Environments

Mobile Mapping Systems (MMS) are integrated platforms that collect spatial data from mobile units, equipped with various sensors. These systems create accurate, high-resolution maps of the environment by capturing data while in motion. Simultaneous Localization and Mapping (SLAM) is a critical technology for MMS, enabling them to navigate and map their surroundings simultaneously. SLAM algorithms use sensor data to construct or update a map of an unknown environment while keeping track of the device's location within that environment. This paper presents the development and prototyping of an autonomous MMS that leverages SLAM technology to deliver precise 3D mapping. The system is centered around the Livox Mid-360 LiDAR sensor, which offers a 360° x 59° Field of View (FOV), 70-m range, and ± 2 cm precision at 10 m. The sensor is integrated with an Intel Core i7-based computing unit running the Robot Operating System (ROS), all mounted on a mobile rover. The SLAM functionality is powered by the FAST-LIO 2.0 algorithm, which combines LiDAR data with Inertial Measurement Unit (IMU) data through an iterated extended Kalman filter, enabling robust navigation even in challenging environments. Additionally, the system incorporates the SC-A-LOAM algorithm for enhanced odometry, global localization, and loop closure, ensuring accurate and drift-corrected mapping over time. The system’s performance will be validated through comparisons with ground truth data from traditional surveying methods and professional SLAM systems. This low-cost, compact MMS demonstrates significant potential for democratizing access to high precision mapping technologies, particularly in environments where traditional systems are prohibitively expensive or difficult to deploy.