LOCATOR: Low-power ORB accelerator for autonomous cars

Simultaneous Localization And Mapping (SLAM) is crucial for autonomous navigation. ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras used for self-driving cars. In this paper, we propose a high-performance, energy-efficient, and functionally accurate hardware accelerator for ORB-SLA...

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Detalles Bibliográficos
Autores: Taranco Serna, Raúl|||0000-0002-1564-4365, Arnau Montañés, José María|||0000-0002-0336-9191, González Colás, Antonio María|||0000-0002-0009-0996
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/380109
Acceso en línea:https://hdl.handle.net/2117/380109
https://dx.doi.org/10.1016/j.jpdc.2022.12.005
Access Level:acceso abierto
Palabra clave:Autonomous vehicles
ORB
ORB-SLAM
Hardware accelerator
Vehicles autònoms
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
Descripción
Sumario:Simultaneous Localization And Mapping (SLAM) is crucial for autonomous navigation. ORB-SLAM is a state-of-the-art Visual SLAM system based on cameras used for self-driving cars. In this paper, we propose a high-performance, energy-efficient, and functionally accurate hardware accelerator for ORB-SLAM, focusing on its most time-consuming stage: Oriented FAST and Rotated BRIEF (ORB) feature extraction. The Rotated BRIEF (rBRIEF) descriptor generation is the main bottleneck in ORB computation, as it exhibits highly irregular access patterns to local on-chip memories causing a high-performance penalty due to bank conflicts. We introduce a technique to find an optimal static pattern to perform parallel accesses to banks based on a genetic algorithm. Furthermore, we propose the combination of an rBRIEF pixel duplication cache, selective ports replication, and pipelining to reduce latency without compromising cost. The accelerator achieves a reduction in energy consumption of 14597× and 9609×, with respect to high-end CPU and GPU platforms, respectively.