A Depth-Buffer-Based Lidar Model With Surface Normal Estimation

Martin Kirchengast*, Daniel Watzenig

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

— Virtual testing and validation of autonomous systems require real-time capable sensor models to couple the system under test with the simulated environment. This paper proposes a lidar modeling approach using back projection of 360 depth images for point cloud computation. Beam incident angles are derived from estimated surface normal vectors and allow for intensity calculation. Furthermore, spatial beam divergence and multiple return modes can be simulated. Using the depth buffer as primary input source facilitates the integration with different environment simulation tools. Virtual Test Drive (VTD) and CARLA serve as example cases for interfacing with the developed model. An analysis of sampling errors resulting from the underlying model principles is presented. Finally, the normal vector estimation precision and the computation time are evaluated.

Original languageEnglish
Pages (from-to)9375-9386
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number8
DOIs
Publication statusPublished - 2024

Keywords

  • Lidar
  • normal vector estimation
  • point cloud
  • sensor model
  • virtual testing

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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