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Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e


Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E.


Mobile mapping systems, systemsLiDAR, Velodyne HDL-32, defected beams, quality control, 3D simulation, point density



  1. Alsadik, B. (2020). Ideal Angular Orientation of Selected 64-Channel Multi Beam Lidars for Mobile Mapping Systems. Remote Sensing, 12(3), 510.
  2. Alsadik, B., & Remondino, F. (2020). Flight Planning for LiDAR-Based UAS Mapping Applications. ISPRS Int. J. Geo-Inf., 9(6), 378.
  3. Blickfeld. Retrieved July 2019, 2019, from
  4. Garnett, R., & Adams, M. (2018). LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis. ISPRS International Journal of Geo-Information, 7, 161. doi: 10.3390/ijgi7050161
  5. Gordon, S. J., & Lichti, D. D. (2004). Terrestrial Laser Scanners with A Narrow Field of View: The Effect on 3D Resection Solutions. Survey Review, 37(292), 448-468. doi: 10.1179/sre.2004.37.292.448
  6. Hesai. Retrieved August 2019, 2019, from
  7. Knaak, T. (2015). Establishing Requirements, Extracting Metrics and Evaluating Quality of LiDAR Data USA.
  8. Luminar. Retrieved July 2019, 2019, from
  9. Maverick. (2020). Retrieved July 2020
  10. Oh, S., You, J.-H., Eskandarian, A., & Kim, Y.-K. (2020). Accurate Alignment Inspection System for Low-resolution Automotive and Mobility LiDAR.
  11. Ouster. Retrieved November 2018, 2018, from
  12. Santiago, R., & Maria, B.-G. (2019). An Overview of Lidar Imaging Systems for Autonomous Vehicles. Applied Sciences, 9(19), 4093.
  13. Shanker, R., Jonas, A., Devitt, S., Huberty, K., Flannery, S., & Greene, W. (2013). Autonomous Cars: Self-Driving the New Auto Industry Paradigm. In M. S. R. Global (Ed.).
  14. TOPCON. (2020). IP-S3 mobile mapping system. Retrieved July 21, 2020, from
  15. Velodyne. Retrieved 1 October, 2018, from
  16. Velodyne. HDL-32E. High Resolution Real-Time 3D Lidar Sensor. Retrieved 20 th of August 2020, from
  17. Viametris. (2020). vMS3D. Retrieved July 2020.