Landmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks

Abstract : Precise localization in dense urban areas is a challenging task for both mobile mapping and driver assistance systems. This paper proposes a strategy to use road markings as localization landmarks for vision based systems. First step consists in reconstructing a map of road marks. A mobile mapping system equipped with precise georeferencing devices is applied to scan the scene in 3D and to generate an ortho-image of the road surface. A RJMCMC sampler that is coupled with a simulated annealing method is applied to detect occurrences of road marking templates instanced from an extensible database of road mark patterns. The detected objects are reconstructed in 3D using the height information obtained from 3D points. A calibrated camera and a low cost GPS receiver are embedded on a vehicle and used as localization devices. Local bundle adjustment (LBA) is applied to estimate the trajectory of the vehicle. In order to reduce the drift of the trajectory, images are matched with the reconstructed road marks frequently. The matching is initialized by the initial poses that are estimated by LBA and optimized by a MCMC algorithm. The matching provides ground control points that are integrated in the LBA in order to refine the pose parameters. The method is evaluated on a set of images acquired in a real urban area and is compared with a precise ground-truth.
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Bahman Soheilian, Xiaozhi Qu, Mathieu Brédif. Landmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks. 2016 IEEE Intelligent Vehicles Symposium (IV), Jun 2016, Gotenburg, Sweden. ⟨10.1109/IVS.2016.7535501⟩. ⟨hal-01882538⟩

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