Monocular SLAM Supported Object Recognition
Name
Leonard_Monocular SLAM.pdf
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6.8 MB
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Adobe PDF
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Author(s)
Pillai, Sudeep
Leonard, John Joseph
Date Issued
July 2015
Journal
Proceedings of the 2015 Robotics: Science and Systems Conference
Citation
Pillai, Sudeep, and John J. Leonard. "Monocular SLAM Supported Object Recognition." 2015 Robotics: Science and Systems Conference (July 2015).
Version
Author's final manuscript
Abstract
In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://www.roboticsproceedings.org/rss11/p34.html
