RT Conference Proceedings
T1 A method for synthetic LiDAR generation to create annotated datasets for autonomous vehicles perception
A1 Beltrán de la Cita, Jorge
A1 Cortes Lafuente, Irene
A1 Barrera Del Pozo, Alejandro
A1 Urdiales de la Parra, Jesús
A1 Guindel Gómez, Carlos
A1 García Fernández, Fernando
A1 Escalera Hueso, Arturo de la
AB LiDAR devices have become a key sensor for autonomous vehicles perception due to their ability to capture reliable geometry information. Indeed, approaches processing LiDAR data have shown an impressive accuracy for 3D object detection tasks, outperforming methods solely based on image inputs. However, the wide diversity of on-board sensor configurations makes the deployment of published algorithms into real platforms a hard task, due to the scarcity of annotated datasets containing laser scans. We present a method to generate new point clouds datasets as captured by a real LiDAR device. The proposed pipeline makes use of multiple frames to perform an accurate 3D reconstruction of the scene in the spherical coordinates system that enables the simulation of the sweeps of a virtual LiDAR sensor, configurable both in location and inner specifications. The similarity between real data and the generated synthetic clouds is assessed through a set of experiments performed using KITTI Depth and Object Benchmarks.
PB IEEE
SN 978-1-5386-7024-8
YR 2019
FD 2019-10-27
LK https://hdl.handle.net/10016/33633
UL https://hdl.handle.net/10016/33633
LA eng
NO Proceedings of: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
NO Research supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21), and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.
DS e-Archivo
RD 18 jul. 2024