Efficient Incremental Map Segmentation in Dense RGB-D Maps
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“Efficient Incremental Map Segmentation in Dense RGB-D Maps” by R. Finman, T. Whelan, M. Kaess, and J.J. Leonard. In IEEE Intl. Conf. on Robotics and Automation, ICRA, (Hong Kong), June 2014.
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.
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BibTeX entry:
@inproceedings{Finman14icra,
author = {R. Finman and T. Whelan and M. Kaess and J.J. Leonard},
title = {Efficient Incremental Map Segmentation in Dense {RGB-D} Maps},
booktitle = {IEEE Intl. Conf. on Robotics and Automation, ICRA},
address = {Hong Kong},
month = {Jun},
year = {2014}
}