2 code implementations • 11 May 2017 • Carlos Guindel, Jorge Beltrán, David Martín, Fernando García
Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on-board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process.
2 code implementations • 12 Jan 2021 • Jorge Beltrán, Carlos Guindel, Arturo de la Escalera, Fernando García
Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding.
2 code implementations • 3 May 2018 • Jorge Beltran, Carlos Guindel, Francisco Miguel Moreno, Daniel Cruzado, Fernando Garcia, Arturo de la Escalera
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision.
2 code implementations • 9 Mar 2020 • Alejandro Barrera, Carlos Guindel, Jorge Beltrán, Fernando García
On-board 3D object detection in autonomous vehicles often relies on geometry information captured by LiDAR devices.
no code implementations • 21 Aug 2020 • Jorge Beltrán, Carlos Guindel, Irene Cortés, Alejandro Barrera, Armando Astudillo, Jesús Urdiales, Mario Álvarez, Farid Bekka, Vicente Milanés, Fernando García
In this paper, a multi-modal 360$^{\circ}$ framework for 3D object detection and tracking for autonomous vehicles is presented.
no code implementations • 22 Apr 2021 • Alejandro Barrera, Jorge Beltrán, Carlos Guindel, Jose Antonio Iglesias, Fernando García
The performance of object detection methods based on LiDAR information is heavily impacted by the availability of training data, usually limited to certain laser devices.