Examinando por Autor "Al-Kaff, Abdulla"

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A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
(MDPI, 2020-08-14) de Miguel, Miguel Ángel; Moreno, Francisco Miguel; Marín-Plaza, Pablo; Al-Kaff, Abdulla; Palos, Martín; Martín, David; Encinar-Martín, Rodrigo; García, Fernando
This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and several autonomous vehicle technologies developed using the framework of this collaboration are introduced and described in this work. Computer vision technologies for environment perception, V2X communication capabilities, enhanced localization, human–machine interaction and self awareness are among the technologies which have been developed and tested. Some use cases that validate the technologies presented in the platform are also presented; these use cases include public demonstrations, tests of the technologies and international competitions for self-driving technologies.
High-Accuracy Patternless Calibration of Multiple 3-D LiDARs for Autonomous Vehicles
(Institute of Electrical and Electronics Engineers, 2023-06-01) de Miguel, Miguel Ángel; Al-Kaff, Abdulla; García, Fernando; Guindel, Carlos
This article proposes a new method for estimating the extrinsic calibration parameters between any pair of multibeam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative closest point (ICP)-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the car learning to act (CARLA) simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib.