This paper deals with the task of ego-vehicle localization at intersections, a significant task in autonomous road driving. We propose an online vision-based method that can hence be applied if the intersection is visible. It relies on stereo images and on a coarse street-level pose estimate, used to retrieve intersection data from a digital map service. Pixel-level semantic segmentation, and 3D reconstruction from state-of-the art Deep Neural Networks are coupled with an intersection model; this allows good positioning accuracy compared to the state-of-the-art in this task. To demonstrate the effectiveness of the method and make it possible to compare it with other methods, an extensive activity has been conducted in order to set up a dataset of approaches to an intersection, which has then been used to benchmark the proposed method. The dataset is made available to the community, and it currently includes more than forty intersection approaches, from KITTI. Another important contribution of the paper is the definition of criteria for the comparison of different methods, on recorded datasets. The proposed method achieves nearly sub-meter accuracy in difficult real conditions.

Ballardini, A., Cattaneo, D., Sorrenti, D. (2019). Visual localization at intersections with digital maps. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp.6651-6657). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICRA.2019.8794413].

Visual localization at intersections with digital maps

Ballardini A. L.;Cattaneo D.;Sorrenti D. G.
2019

Abstract

This paper deals with the task of ego-vehicle localization at intersections, a significant task in autonomous road driving. We propose an online vision-based method that can hence be applied if the intersection is visible. It relies on stereo images and on a coarse street-level pose estimate, used to retrieve intersection data from a digital map service. Pixel-level semantic segmentation, and 3D reconstruction from state-of-the art Deep Neural Networks are coupled with an intersection model; this allows good positioning accuracy compared to the state-of-the-art in this task. To demonstrate the effectiveness of the method and make it possible to compare it with other methods, an extensive activity has been conducted in order to set up a dataset of approaches to an intersection, which has then been used to benchmark the proposed method. The dataset is made available to the community, and it currently includes more than forty intersection approaches, from KITTI. Another important contribution of the paper is the definition of criteria for the comparison of different methods, on recorded datasets. The proposed method achieves nearly sub-meter accuracy in difficult real conditions.
poster + paper
Control and Systems Engineering; Software; Artificial Intelligence; Electrical and Electronic Engineering; autonomous driving; robotics; autonomous vehicles; road detection; classification; intersection
English
2019 International Conference on Robotics and Automation, ICRA 2019
2019
2019 International Conference on Robotics and Automation, ICRA 2019
978-153866026-3
2019
2019-
6651
6657
8794413
https://ieeexplore.ieee.org/abstract/document/8794413
reserved
Ballardini, A., Cattaneo, D., Sorrenti, D. (2019). Visual localization at intersections with digital maps. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp.6651-6657). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICRA.2019.8794413].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/242038
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