Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning

Stefan Milz, Georg Arbeiter, Christian Witt, Bassam Abdallah, Senthil Yogamani; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 247-257

Abstract


Deep learning has become the standard model for object detection and recognition. Recently, there is progress on using CNN models for geometric vision tasks like depth estimation, optical flow prediction or motion segmentation. However, Visual SLAM remains to be one of the areas of automated driving where CNNs are not mature for deployment in commercial automated driving systems. In this paper, we explore how deep learning can be used to replace parts of the classical Visual SLAM pipeline. Firstly, we describe the building blocks of Visual SLAM pipeline composed of standard geometric vision tasks. Then we provide an overview of Visual SLAM use cases for automated driving based on the authors' experience in commercial deployment. Finally, we discuss the opportunities of using Deep Learning to improve upon state-of-the-art classical methods.

Related Material


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[bibtex]
@InProceedings{Milz_2018_CVPR_Workshops,
author = {Milz, Stefan and Arbeiter, Georg and Witt, Christian and Abdallah, Bassam and Yogamani, Senthil},
title = {Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}