Road condition analysis is an important research topic in many fields (such as intelligent transportation, road safety, road design analysis, and traffic analysis) and depends on road geometry parameters such as longitudinal profile and cross-slope. In this study, the extraction of road geometry parameters by unmanned aerial vehicle (UAV) with LiDAR and by a mobile photogrammetric system (MPS) designed by our research group was investigated. The purpose of this study was to obtain geometric parameters (such as road longitudinal profile and cross-slope) by using digital terrain model (DTM) surfaces derived from point cloud data acquired using UAV-LiDAR and MPS. For this purpose, a framework was developed for the extraction and comparison of longitudinal and cross-sectional profiles. First, the ground filtering approach was used to extract ground points and DTM surfaces generated from an appropriate interpolation algorithm by using ground points. Cross-sectional/longitudinal profiles of the road sections were extracted and compared with reference data. A comparison of the longitudinal profiles obtained from DTMs derived from the MPS and from UAV-LiDAR revealed root mean square error values of 1.8 cm and 2.3 cm, respectively. The average deviation of cross-slopes for both surfaces was 0.19% and 0.18%, respectively. These results show that road geometric parameters can be obtained from DTM surfaces with high accuracy. It can be concluded from the results of this study that MPS can be a favorable alternative for studies on road geometry parameters extraction.
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.
The density, high accuracy, and rapid collection of geographical data for road surface and surrounding objects and the extraction of meaningful information from these data increases its importance in line with technological developments. Artificial intelligence studies and developments in cloud technology have affected the automotive industry as well as every sector and have enabled the development of driverless vehicle technology. In order to safely drive with autonomous vehicles, high definition maps that contain detailed information for road surface and its surrounding objects with high precision at centimeter-level must be used. In this context, in recent years, the development of mobile mapping systems (MMS) consisting of low-cost sensors and the development of algorithms for the evaluation of the data obtained from these systems have become increasingly popular. In this study, it was investigated whether HD maps can be obtained by using low-cost imaging sensors.
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