Efficient Incremental Map Segmentation in Dense RGB-D Maps

Ross Finman, Thomas Whelan, Michael Kaess and John J. Leonard
Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, (Hong Kong), June, 2014

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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.

To appear June 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)},
year = {2014},
month = {June},
} 2017-09-13T10:39:00-04:00