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Bridging text spotting and SLAM with junction features

Author(s)
Finn, Chelsea; Kaess, Michael; Teller, Seth; Wang, Hsueh-Cheng; Paull, Liam; Rosenholtz, Ruth Ellen; Leonard, John J; ... Show more Show less
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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.
Date issued
2015
URI
http://hdl.handle.net/1721.1/107466
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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
ISBN
978-1-4799-9994-1

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