Efficient incremental map segmentation in dense RGB-D maps
Author(s)Whelan, Thomas; Kaess, Michael; Finman, Ross Edward; Leonard, John Joseph
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mechanical Engineering
Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA)
Institute of Electrical and Electronics Engineers (IEEE)
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).
Author's final manuscript