To address the issue that single-frame perspective data struggle to depict the complete contour of large-scale targets, thereby limiting size measurement, this article proposed a large-target size measurement method of LiDAR based on visual semantic constraints, with automobiles as the research object for large-target measurement. Firstly, this method achieves spatio-temporal synchronization of LiDAR, camera, and inertial measurement unit data through joint calibration and timestamp nearest-neighbor matching. Subsequently, a mobile cart is used to acquire information about the entire target in three dimensions. The simultaneous localization and mapping technology is employed to reconstruct the contour of the measured target. In this module, the algorithm′s accuracy is enhanced through a ground-based residual optimization and loop closure detection framework. After denoising the point cloud, a ground segmentation algorithm is used to separate ground points from non-ground points, and a pass-through filter is applied to ensure the segmentation effect. Meanwhile, a target detection algorithm is utilized to obtain the category and position information of the target in the image. Next, through an adaptive threshold point cloud clustering method, the centers of different point cloud clusters are visually projected, and the point cloud corresponding to the target is located according to the visual target detection results. Finally, a contour fitting algorithm is designed to complete the contour fitting of the target point cloud. Then, a three-dimensional box fitting algorithm is used to calculate the target′s size. Experimental results show that for large-sized objects such as automobiles. In a parking lot with a large number of vehicles, the proposed method yields a maximum error and an average error of less than 1.97% and 0.82% respectively for vehicle length, less than 3.26% and 2.08% respectively for width, and less than 3.99% and 1.99% respectively for height, demonstrating promising prospects for engineering applications.