A framework for outdoor urban environment estimation

In this paper we present a general framework for urban road layout estimation, altogether with a specific application to the vehicle localization problem. The localization is performed by synergically exploiting data from different sensors, as well as map-matching with OpenStreetMap cartographic maps. The effectiveness is proven by achieving real-time com- putation with state-of-the-art results on a set of ten not trivial runs from the KITTI dataset, including both urban/residential and highway/road scenarios. Although this paper represents a first step implementation towards a more general urban scene understanding framework, here we prove its flexibility of appli- cation to different intelligent vehicles applications.

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author={A. L. Ballardini and S. Fontana and A. Furlan and D. Limongi and D. G. Sorrenti},
booktitle={2015 IEEE 18th International Conference on Intelligent Transportation Systems},
title={A Framework for Outdoor Urban Environment Estimation},
keywords={cartography;data handling;KITTI dataset;OpenStreetMap cartographic map;highway-road scenario;intelligent vehicles application;outdoor urban environment estimation framework;urban road layout estimation;urban scene understanding framework;urban-residential scenario;vehicle localization problem;Buildings;Global Positioning System;Layout;Probabilistic logic;Roads;Sensors;Vehicles},