Abstract: In light of the challenges associated with relying on manual experience, low measurement efficiency, and potential omission of key dimensions in existing house size measurement methods, this paper proposes an intelligent room measurement method based on a single-line laser radar. Initially, efficient and accurate collection of two-dimensional point cloud data from the surrounding walls is achieved by holding a single-line laser radar and walking at a constant speed indoors. Subsequently, the Euclidean clustering method is employed to segment relatively independent subsets of point clouds from the initial scanned point cloud, alleviating the computational load of the point cloud. Following that, using the Hough transform method, contour features such as line segments and circular arcs are extracted from the point cloud subset. Finally, the point cloud and contour line features are superimposed and matched, and the connection relationships of the features are corrected according to the connection law of the house wall. Additionally, closed-loop detection is performed to obtain orderly connected wall contour lines. The results showed that the overall time consumption of the method proposed in this paper was about 191 s, indicating that the measurement efficiency is nearly 5 times higher than traditional tape measure or laser rangefinder measurements. The measurement accuracy was approximately 2 cm, and the contour line extraction accuracy was about 1.2 cm, representing an improvement over tape measurement. The method boasts low equipment cost, high data integrity, and minimal human interference factors, effectively enabling rapid measurement of housing layouts and the automatic generation of layout maps. It can offer valuable data support for subsequent engineering applications, such as decoration construction and spatial asset management.