Structural Health Monitoring (SHM) requires reliable spatial data to support the assessment of infrastructure conditions and potential geometric inconsistencies. Recent advances in mobile and low-cost geospatial technologies, including LiDAR-equipped smartphones and handheld SLAM-based systems, have enabled rapid and flexible point cloud acquisition, although their achievable accuracy in operational scenarios remains a key question. This study presents a comparative analysis of point clouds acquired using an iPhone 15 Pro Max equipped with a viDoc RTK Rover and a Leica BLK2GO same spatial LiDAR system, against a terrestrial laser scanning (TLS) reference dataset collected with a Leica RTC360. All datasets were georeferenced within a common GNSS-based reference framework using RTK corrections and ground control points measured with a Leica GS15 receiver. The comparison focuses on the geometric agreement between datasets through cloud-to-cloud distance analysis and the evaluation of vertical and horizontal point cloud differences. Statistical indicators derived from open-source processing environments are used to characterize the spatial consistency of the mobile acquisitions with respect to the reference model. The results highlight the potential of both smartphone-based and SLAM-based LiDAR systems for rapid, first-level infrastructure inspections, particularly in contexts where fast data acquisition, ease of use, and open-source workflows are prioritized over high-precision deformation monitoring. The study provides practical insights into the advantages and limitations of low-cost and mobile LiDAR technologies for operational SHM applications.