Bridging text spotting and SLAM with junction features
Name
Leonard_Bridging text.pdf
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1.72 MB
Format
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Author(s)
Finn, Chelsea
Kaess, Michael
Teller, Seth
Wang, Hsueh-Cheng
Paull, Liam
Rosenholtz, Ruth Ellen
Leonard, John J
Date Issued
2015
Journal
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wang, Hsueh-Cheng, Chelsea Finn, Liam Paull, Michael Kaess, Ruth Rosenholtz, Seth Teller, and John Leonard. “Bridging Text Spotting and SLAM with Junction Features.” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (September 2015). ©2015 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Navigating in a previously unknown environment and recognizing naturally occurring text in a scene are two important autonomous capabilities that are typically treated as distinct. However, these two tasks are potentially complementary, (i) scene and pose priors can benefit text spotting, and (ii) the ability to identify and associate text features can benefit navigation accuracy through loop closures. Previous approaches to autonomous text spotting typically require significant training data and are too slow for real-time implementation. In this work, we propose a novel high-level feature descriptor, the “junction”, which is particularly well-suited to text representation and is also fast to compute. We show that we are able to improve SLAM through text spotting on datasets collected with a Google Tango, illustrating how location priors enable improved loop closure with text features.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IROS.2015.7353895
