A LiDAR system simulator based on raytracing, modeled with metrological parameters and environmental noise
Resumo
The widespread use of point clouds in many applications is prompting many researchers to find new ways to generate such datasets, as the technology is still costly and slow depending on the experiment’s parameters. Virtual scanning, a computer simulation of scanning a real target, is one of these solutions. This paper is a short presentation of the current development of a light detection and ranging (LiDAR) system simulator based on metrological parameters, sensor error model and light scattering treatment.
Referências
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F. Wang, Y. Zhuang, H. Gu and H. Hu, "Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis," in IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 7, pp. 2671-2673, July 2019.
G. Gusmão, "A LiDAR system simulator using parallel raytracing and validated by comparison with a real sensor," to be present at CBM 2019.
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
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Q. Fang and D. A. Boas, "Monte Carlo Simulation of Photon Migration in 3D Turbid Media Accelerated by Graphics Processing Units," Optics Express, vol. 17, no. 22, p. 20178, 2009
A. Tallavajhula,"Lidar simulation for robotic application development: modeling and evaluation," Ph.D. dissertation, The Robotics Institute, Carnegie Mellon University, Pittsburgh, 2018
T. Hanke, A. Schaermann, M. Geiger, K. Weiler, N. Hirsenkorn, A. Rauch, S.-A. Schneider, and E. Biebl, "Generation and validation of virtual point cloud data for automated driving systems," 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017.
F. Wang, Y. Zhuang, H. Gu and H. Hu, "Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis," in IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 7, pp. 2671-2673, July 2019.
G. Gusmão, "A LiDAR system simulator using parallel raytracing and validated by comparison with a real sensor," to be present at CBM 2019.
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
N. Hirsenkorn, H. Kolsi, M. Selmi, A. Schaermann, T. Hanke, A. Rauch, R. Rasshofer and E. Biebl, "Learning Sensor Models for Virtual Test and Development," in Workshop Fahrerassistenz und automatisiertes Fahren, 2017.
Q. Fang and D. A. Boas, "Monte Carlo Simulation of Photon Migration in 3D Turbid Media Accelerated by Graphics Processing Units," Optics Express, vol. 17, no. 22, p. 20178, 2009
Publicado
16/10/2019
Como Citar
GUSMÃO, Guilherme Ferreira; BARBOSA, Carlos Roberto Hall; RAPOSO, Alberto Barbosa.
A LiDAR system simulator based on raytracing, modeled with metrological parameters and environmental noise. In: WORKSHOP DE TESES E DISSERTAÇÕES - SIMPÓSIO DE REALIDADE VIRTUAL E AUMENTADA (SVR), 21. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 11-12.
DOI: https://doi.org/10.5753/svr_estendido.2019.8453.
