Road Infrastructure Mapping by Using Iphone 14 Pro: An Accuracy Assessment

Abstract

Abstract. Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

“…The vertical and horizontal clearances of highway viaducts and gantries can be automatically estimated using MLS point clouds for routing oversized transport items, infrastructure reconstruction, maintenance, and settling legal claims after incidents [90]. The point cloud data of road infrastructure was mapped via the built-in camera and LiDAR sensors integrated into the iPhone 14 Pro using real-time kinematic positioning systems, which consequently improved the geo-referencing accuracy [91]. For the maintenance of existing railway infrastructure, a platform that combines 2D panoramic virtual reality photos and a 3D model generated using a 3D scanner has also been demonstrated [18].…”