Efficient incremental map segmentation in dense RGB-D maps

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.

Authors

• Ross Finman
• Thomas Whelan
• Michael Kaess
• John J. Leonard

Keywords

• Silicon
• Complexity theory
• Image segmentation
• Real-time systems
• Timing
• Simultaneous localization and mapping
• Density Map
• Segmentation Map
• Object Detection
• Segmentation Method
• Size Of Map
• Millions Of Points
• 3D Reconstruction
• Point Cloud
• Segmentation Algorithm
• Small Segments
• Segment Size
• Previous Segment
• Incremental Algorithm
• Surface Normals