Ross Finman, Thomas Whelan, Michael Kaess, and John J. Leonard
IEEE Intl. Conf. on Robotics and Automation, ICRA, (Hong Kong), July, 2014.
|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.|
Note: To appear June 2014
|Ross Finman, Thomas Whelan, Michael Kaess, and John J. Leonard, "Efficient Incremental Map Segmentation in Dense RGB-D Maps," IEEE Intl. Conf. on Robotics and Automation, ICRA, (Hong Kong), July, 2014.|
author = "Ross Finman and Thomas Whelan and Michael Kaess and John J. Leonard",
title = "Efficient Incremental Map Segmentation in Dense RGB-D Maps",
booktitle = "IEEE Intl. Conf. on Robotics and Automation, ICRA, (Hong Kong)",
month = "July",
year = "2014",
Notes = "To appear June 2014"
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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