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Communication Dans Un Congrès Année : 2014

Lane Marking Based Vehicle Localization Using Particle Filter and Multi-Kernel Estimation

Résumé

Vehicle localization is the primary information needed for advanced tasks like navigation. This information is usually provided by the use of Global Positioning System (GPS) receivers. However, the low accuracy of GPS in urban environments makes it unreliable for further treatments. The combination of GPS data and additional sensors can improve the localization precision. In this article, a marking feature based vehicle localization method is proposed, able to enhance the lo-calization performance. To this end, markings are detected using a multi-kernel estimation method from an on-vehicle camera. A particle filter is implemented to estimate the vehicle position with respect to the detected markings. Then, map-based markings are constructed according to an open source map database. Finally, vision-based markings and map-based markings are fused to obtain the improved vehicle fix. The results on road traffic scenarios using a public database show that our method leads to a clear improvement in localization accuracy.
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Dates et versions

hal-01095614 , version 1 (15-12-2014)

Identifiants

  • HAL Id : hal-01095614 , version 1

Citer

Wenjie Lu, Emmanuel Seignez, Sergio Alberto Rodriguez Florez, Roger Reynaud. Lane Marking Based Vehicle Localization Using Particle Filter and Multi-Kernel Estimation. The 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Dec 2014, Marina Bay Sands, Singapore. ⟨hal-01095614⟩
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