Beyond high-definition maps: map alternatives for intelligent vehicles\' localization

• Ano de defesa: 2024
• Autor(a) principal: Przewodowski Filho, Carlos André Braile
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Autonomous vehicles; Digital map; Localização; Localization; Mapas Aéreos; OpenStreetMap; Semantic; SLAM; Veículos Inteligentes
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-06012025-124638/

State estimation plays an important role in most navigation tasks for mobile robots. In the case of autonomous vehicles, localization is primarily performed using measurements from Global Navigation Satellite System (GNSS) devices, high-definition (HD) maps, or both. On the other hand, aerial and derived maps, or higher-level maps, such as OpenStreetMap or Google Maps, represent more financially viable alternatives and, depending on how they are used, with more stable characteristics. However, the task of associating low-level sensor measurements with high-level features of these maps tends to be more complex given that the data representation domains tend to be different. Therefore, in this thesis, we propose to combine techniques to associate low-level sensor data with a more abstract map for localization in the context of intelligent vehicles. Among the results obtained with the last of the proposed methods, we achieved a localization with an average error of 3:4m using the trajectories from the Kitti dataset, surpassing the popular method proposed in (MILLER et al., 2021) by more than 1:7m on the same dataset. Finally, the methods proposed in this thesis aimed at modularization and integration with more complex systems, so that - even without the intention of merging with HD or GNSS maps - these can be integrated into the same system.