Efficient incremental map segmentation in dense RGB-D maps

• Main Authors: Whelan, Thomas (Author), Kaess, Michael (Author), Finman, Ross Edward (Contributor), Leonard, John Joseph (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2015-06-30T15:33:22Z.
• Subjects: Article
• Online Access: Get fulltext

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.
United States. Office of Naval Research (Grant N00014-10-1-0936)
United States. Office of Naval Research (Grant N00014-11-1-0688)
United States. Office of Naval Research (Grant N00014-12-10020)
National Science Foundation (U.S.) (Grant IIS-1318392)
Science Foundation Ireland (Strategic Research Cluster Grant 07/SRC/I1168)