Abstract

The aim of this paper is to contribute with an object-based learning and selection methods to improve localization and mapping techniques. The methods use 3D-LiDAR data which is suitable for autonomous driving systems operating in urban environments. The objects of interest to be served as landmarks are pole-like objects which are naturally present in the environment. To detect and recognize pole-like objects in 3D-LiDAR data, a semi-supervised iterative label propagation method has been developed. Additionally, a selection method is proposed for selection the best poles to be used in the localization loop. The LiDAR localization and mapping system is validated using data from the KITTI database. Reported results show that by considering the occurrence of pole-like objects over time leads to an improvement on both the learning model and the localization.

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