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Article Dans Une Revue International Scientific Journal Engineering Applications of Artificial Intelligence Année : 2021

Feature-refined Box Particle Filtering for Autonomous Vehicle Localisation with OpenStreetMap

Résumé

Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle selflocalisation in the presence of sensor and map uncertainties. The proposed feature-refined box particle filter incorporates line features extracted from a multi-layer Light Detection And Ranging (LiDAR) sensor and information from OpenStreetMap to estimate the vehicle state. A particle weight balance strategy is incorporated to account for the OpenStreetMap inaccuracy, which is assessed by comparing it to a high definition road map. The performance of the proposed framework is evaluated on a LiDAR dataset and compared with box particle filter variants. Experimental results show that the proposed framework achieves respectively 10% and 53% localisation accuracy improvement with reduced box volumes of 25% and 41%, when compared with the state-of-the-art interval analysis based box regularisation particle filter and the box particle filter.
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Dates et versions

hal-03521436 , version 1 (11-01-2022)

Identifiants

Citer

Peng Wang, Lyudmila Mihaylova, Philippe Bonnifait, Philippe Xu, Jianwen Jiang. Feature-refined Box Particle Filtering for Autonomous Vehicle Localisation with OpenStreetMap. International Scientific Journal Engineering Applications of Artificial Intelligence, 2021, 105, pp.10445. ⟨10.1016/j.engappai.2021.104445⟩. ⟨hal-03521436⟩
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