Vehicle Pose Estimation In Cluttered Urban Environments Using Multilayer Adaptive Monte Carlo Localization
2016 19th International Conference on Information Fusion (FUSION)(2016)
摘要
In this contribution, we propose multilayer adaptive Monte Carlo localization (ML-AMCL) in combination with 3D point registration algorithms as a GPS-independent framework for precise global vehicle pose estimation in challenging urban environments. Scans from a 3D LIDAR sensor are split into a set of horizontal layers which are then used for localization with separate instances of an AMCL algorithm. A consistency check is performed for the obtained pose estimates in every time step and feasible results are fused. It is shown, that ML-AMCL is superior to existing localization approaches and is well suited as a prior for 3D point registration algorithms for the refinement of the pose estimate. Our key contributions are: i) proposal of ML-AMCL. ii) Incorporation of prior information from ML-AMCL into different pose refinement procedures, i.e. ICP variants and normal distribution transform (NDT), for precise vehicle localization. By means of experimental evaluation with a challenging real data set, performance of the reference localization system is verified. The proposed localization framework achieves a mean Euclidean measurement error of 0 : 2 m under severe adverse environment conditions. The latter include high clutter densities in the sensor measurements and semi-static objects in the localization map.
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关键词
urban environments,multilayer adaptive Monte Carlo localization,3D point registration algorithms,GPS,global vehicle pose estimation,3D LIDAR sensor,AMCL algorithm,normal distribution transform,vehicle localization,Euclidean measurement error,sensor measurements,semi-static objects
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