<p>Multi-modal sensor fusion-based LiDAR SLAM is a key capability for reliable mobile robot operation in complex indoor environments. However, it remains susceptible to localization drift and global inconsistency in typical degenerate scenarios such as feature sparsity, repetitive structures, and dynamic disturbances. To address these challenges, we propose a LiDAR-inertial SLAM method enhanced with visual QR-code landmarks. The front-end employs a lightweight EKF-based LiDAR-IMU odometry to ensure real-time and robust motion estimation, while the back-end constructs a unified factor graph that tightly couples LiDAR, IMU, loop-closure, and QR-code landmark factors within a single state space to achieve globally consistent cross-modal constraints. QR codes are further incorporated as persistent artificial landmarks to provide strong global anchoring in long corridors and repetitive or feature-degraded environments. In addition, an adaptive covariance and hierarchical weighting mechanism dynamically adjusts factor influence based on residual statistics and observation quality, thereby improving robustness under occlusion, degradation, and sensor noise variations. Experimental results demonstrate that the proposed system significantly improves localization accuracy and mapping stability across various challenging indoor scenarios. These findings validate the effectiveness of deeply integrating visual landmarks with LiDAR-inertial information, offering new scientific evidence and practical value for robust multi-modal SLAM in indoor robotic perception—fully aligning with the research scope of Scientific Reports.</p>