Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning
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.
Cite
Text
Milz et al. "Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00062Markdown
[Milz et al. "Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/milz2018cvprw-visual/) doi:10.1109/CVPRW.2018.00062BibTeX
@inproceedings{milz2018cvprw-visual,
title = {{Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning}},
author = {Milz, Stefan and Arbeiter, Georg and Witt, Christian and Abdallah, Bassam and Yogamani, Senthil Kumar},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {247-257},
doi = {10.1109/CVPRW.2018.00062},
url = {https://mlanthology.org/cvprw/2018/milz2018cvprw-visual/}
}