Deep Visual Teach and Repeat on Path Networks
Name
Swedish_Deep_Visual_Teach_CVPR_2018_paper.pdf
Description
Published version
Size
695.88 KB
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Unknown
Checksum (MD5)
379608b80274da0cf02800d8af1eb097
Author(s)
Swedish, Tristan
Raskar, Ramesh
Date Issued
June 2018
Publisher
IEEE
Citation
Swedish, Tristan and Raskar, Ramesh. 2018. "Deep Visual Teach and Repeat on Path Networks."
Version
Final published version
Abstract
© 2018 IEEE. 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvprw.2018.00203
