Comparative Evaluation of LiDAR Systems for Transport Infrastructure: Case Studies and Performance Analysis

Mobile laser scanners play a crucial role in intelligent transport infrastructure as they capture highly detailed 3D representations of the road environment. However, the accuracy of MLS data is influenced by various factors such as the positioning and orientation sensors, as well as the capture environment. In this regard, this paper is focused on comparing two Mobile Laser Scanners mounted on a van (Optech Lynx Mobile Mapper and Riegl VUX-1HA) and one Terrestrial Laser Scanner Faro Focus XX30. The study utilizes point cloud reference data from the Faro Focus XX30 terrestrial laser scanner and GNSS reference data from the Trimble R8. The performance of the mobile laser scanners is evaluated in three distinct scenarios: road environment, urban environment, and semi-urban environment. To assess accuracy, the difference between Trimble GNSS and MLS coordinates is measured in absolute terms. The geometric features of each LiDAR are then calculated and compared. Additionally, the utility of the three LiDAR is examined through mapping tasks in the road and urban environments, utilizing a machine learning classifier. The results demonstrate that the Riegl scanner achieves satisfactory accuracy in the road and semi-urban environments, while the Faro scanner performs better in urban environments in terms of classification accuracy. Moreover, the thorough analysis provides convincing evidence that Riegl achieves superior classification accuracy in road environments, while Faro excels in urban environments. The findings presented in this paper offer valuable insights for researchers and professionals in selecting a mobile laser scanner for mapping transport infrastructure. The comparative analysis of MLS systems, along with their performance evaluation in different environments, aids decision-making processes in the field of intelligent transport infrastructure mapping.