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Precise self-localization of autonomous vehicles using lidar sensors and highly accurate digital maps on highway roads

Abstract : In this thesis, we address the problem of accurate localization of autonomous vehicles on highway roads using LiDAR sensors and a highly accurate third party map. The proposed approach is based on two core modules: perception and map-matching. The perception module uses the 3D data enhanced by the LiDAR reflectivity to detect road primitive features: lane markings, barriers, traffic signs and guardrail reflectors. The map-matching module incorporates these measurements and aligns them against a highly accurate third party map. The map-matching is performed using a particle filter, which we have improved in order to deal with the particle deprivation problem. The proposed improvement uses the road geometry in order to optimize the spatial distribution of particles while maintaining the number of particles constant. To evaluate the proposed method, we compared the localization outputs of our system to a Global Navigation Satellite System (GNSS) with RTK corrections (ground truth). Experiments have been conducted on two highway roads. The first is an experimental test track (CTA2) of 5 km long located at CTA, Renault’s Aubevoye’s Technical Center. This track is designed to exactly replicate a two-lane highway environment. The second is a section of the A13 highway, running from Paris and ending at Aubevoye. The results are promising and show the feasibility of a localization system based on LiDARs alone and with a sparse map data representation.
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Contributor : Abes Star :  Contact
Submitted on : Thursday, December 10, 2020 - 3:01:32 PM
Last modification on : Thursday, February 3, 2022 - 11:14:22 AM
Long-term archiving on: : Thursday, March 11, 2021 - 7:45:52 PM


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  • HAL Id : tel-03052168, version 1


Farouk Ghallabi. Precise self-localization of autonomous vehicles using lidar sensors and highly accurate digital maps on highway roads. Robotics [cs.RO]. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLM028⟩. ⟨tel-03052168⟩



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