DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks

Engel N, Hoermann S, Horn M, Belagiannis V, Dietmayer K (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 926-933

Conference Proceedings Title: 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Event location: Auckland, NZL

ISBN: 9781538670248

DOI: 10.1109/ITSC.2019.8917005

Abstract

We address the problem of vehicle selflocalization from multi-modal sensor information and a reference map. The map is generated off-line by extracting landmarks from the vehicle's field of view, while the measurements are collected similarly on the fly. Our goal is to determine the autonomous vehicle's pose from the landmark measurements and map landmarks. To learn this mapping, we propose DeepLocalization, a deep neural network that regresses the vehicle's translation and rotation parameters from unordered and dynamic input landmarks. The proposed network architecture is robust to changes of the dynamic environment and can cope with a small number of extracted landmarks. During the training process we rely on synthetically generated ground-truth. In our experiments, we evaluate two inference approaches in real-world scenarios. We show that DeepLocalization can be combined with regular GPS signals and filtering algorithms such as the extended Kalman filter. Our approach achieves state-of-the-art accuracy and is about ten times faster than the related work.

Authors with CRIS profile

Vasileios Belagiannis

Involved external institutions

Universität Ulm DE Germany (DE)

How to cite

APA:

Engel, N., Hoermann, S., Horn, M., Belagiannis, V., & Dietmayer, K. (2019). DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 926-933). Auckland, NZL: Institute of Electrical and Electronics Engineers Inc..

MLA:

Engel, Nico, et al. "DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks." Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, NZL Institute of Electrical and Electronics Engineers Inc., 2019. 926-933.

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