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