Solid-state LiDARs (SSLs), due to their limited horizontal field of view (FoV), suffer from degeneracy in indoor environments. In response, we propose placing reflectors in degraded regions and incorporating reflector feature residuals into the LiDAR Odometry and Mapping (LOAM) framework. This is combined with an Extended Kalman Filter (EKF) to fuse odometry predictions, reflector observations, and LOAM measurements, effectively mitigating localization drift in the submap. By employing CostMap-Multilateration-based loop closure detection and keyframe selection strategies focused on high-reflection regions of interest (ROIs), our approach achieves robust submap-to-submap registration through a coarse-to-fine process. This ensures globally consistent map construction. To validate the proposed method, qualitative and quantitative evaluations were conducted in real-world environments. The results show that in degenerate indoor environments, the performance of our approach is comparable to, and even exceeds, that of state-of-the-art SLAM methods.
