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Feature-refined box particle filtering for autonomous vehicle localisation with OpenStreetMap.
- Source :
-
Engineering Applications of Artificial Intelligence . Oct2021, Vol. 105, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- 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. • The FRBPF is proposed to deal with OpenStreetMap and sensor data uncertainties. • A contraction algorithm is developed to reduce the volume of box particles. • Theoretical proofs about the features-refined contractions are derived. • A weight balance strategy is designed to improve the performance of FRBPF. [ABSTRACT FROM AUTHOR]
- Subjects :
- *OPTICAL radar
*LIDAR
*AIR filters
*AUTONOMOUS vehicles
*INTERVAL analysis
*ROAD maps
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 105
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 152465516
- Full Text :
- https://doi.org/10.1016/j.engappai.2021.104445