Feature-refined box particle filtering for autonomous vehicle localisation with OpenStreetMap
| Autor: | Philippe Xu, Philippe Bonnifait, Jianwen Jiang, Peng Wang, Lyudmila Mihaylova |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Ranging Interval arithmetic Lidar Artificial Intelligence Control and Systems Engineering Feature (computer vision) Line (geometry) Particle Computer vision Artificial intelligence Electrical and Electronic Engineering Performance improvement business Particle filter |
| ISSN: | 0952-1976 |
| Popis: | Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle self-localisation 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 vehicle states. A particle weight balance strategy is incorporated to account for the OpenStreetMap positional 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 performance 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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