<p>Loop-closure detection is a fundamental challenge in simultaneous localization and mapping (SLAM). LiDAR sensors offer advantages, such as field-of-view (FOV) and robustness to perceptual variations, making them widely adopted for loop-closure detection tasks. However, the limited horizontal and vertical resolution of LiDAR reduces the robustness of lightweight loop-closure detection methods based on 3D point cloud histograms or bird-eye-view (BEV) representations. Moreover, conventional lightweight methods struggle to accurately recognize environments containing both large- and small-scale structures or those with uniform building heights. To overcome these limitations, this study proposes a line shape descriptor approach. The method constructs descriptors by characterizing the geometric shape and scale of each LiDAR line and evaluates similarity by computing the mean cosine distance between corresponding line descriptors across point clouds. To mitigate line drift caused by LiDAR motion or variations in the ground slope at loop-closure locations, a descriptor alignment technique based on a partitioned line sliding window is introduced. Furthermore, a two-stage search algorithm is incorporated to improve detection efficiency. Experimental evaluations on public and self-collected datasets demonstrate that the proposed method achieves significant improvements in loop-closure detection accuracy and robustness.</p>