Efficient incremental map segmentation in dense RGB-D maps
Name
Leonard_Efficient incremental.pdf
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2.93 MB
Format
Adobe PDF
Checksum (MD5)
9ad9de2f2b7243e75876f71793cdc1f4
Author(s)
Whelan, Thomas
Kaess, Michael
Finman, Ross Edward
Leonard, John Joseph
Date Issued
May 2014
Journal
Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Finman, Ross, Thomas Whelan, Michael Kaess, and John J. Leonard. “Efficient Incremental Map Segmentation in Dense RGB-D Maps.” 2014 IEEE International Conference on Robotics and Automation (ICRA) (May 2014).
Version
Author's final manuscript
Abstract
In this paper we present a method for incrementally segmenting large RGB-D maps as they are being created. Recent advances in dense RGB-D mapping have led to maps of increasing size and density. Segmentation of these raw maps is a first step for higher-level tasks such as object detection. Current popular methods of segmentation scale linearly with the size of the map and generally include all points. Our method takes a previously segmented map and segments new data added to that map incrementally online. Segments in the existing map are re-segmented with the new data based on an iterative voting method. Our segmentation method works in maps with loops to combine partial segmentations from each traversal into a complete segmentation model. We verify our algorithm on multiple real-world datasets spanning many meters and millions of points in real-time. We compare our method against a popular batch segmentation method for accuracy and timing complexity.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/ICRA.2014.6907666
