ML Anthology

Deep Visual Teach and Repeat on Path Networks

Tristan Swedish, Ramesh Raskar
CVPRW 2018 pp. 1533-1542
doi:10.1109/CVPRW.2018.00203 /cvprw/2018/swedish2018cvprw-deep/

Abstract

We propose an approach for solving Visual Teach and Repeat tasks for routes that consist of discrete directions along path networks using deep learning. Visual paths are specified by a single monocular image sequence and our approach does not query frames or image features during inference, but instead is composed of classifiers trained on each path. Our method is efficient for both storing or following paths and enables sharing of visual path specifications between parties without sharing visual data explicitly. We evaluate our approach in a simulated environment, and present qualitative results on real data captured with a smartphone.

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Cite

Text

Swedish and Raskar. "Deep Visual Teach and Repeat on Path Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00203

Markdown

[Swedish and Raskar. "Deep Visual Teach and Repeat on Path Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/swedish2018cvprw-deep/) doi:10.1109/CVPRW.2018.00203

BibTeX

@inproceedings{swedish2018cvprw-deep,
  title     = {{Deep Visual Teach and Repeat on Path Networks}},
  author    = {Swedish, Tristan and Raskar, Ramesh},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {1533-1542},
  doi       = {10.1109/CVPRW.2018.00203},
  url       = {https://mlanthology.org/cvprw/2018/swedish2018cvprw-deep/}
}