An AGV Mapping Method Based on Improved LIO-SAM

In some highly complex and dynamically changing operational scenarios, traditional positioning and navigation methods for AGV struggle to meet the real-time, efficient, and safe delivery requirements. Therefore, addressing the SLAM (Simultaneous Localization and Mapping) mapping problem for AGV, this paper designs an improved LIO-SAM (Lidar Inertial Odometry via Smoothing and Mapping) algorithm that tightly integrates LiDAR and IMU (Inertial Measurement Unit). This provides a flexible and real-time mapping solution for logistics and delivery AGVs in complex scenarios.