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

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.