Plane Segmentation in Organized Point Clouds using Flood Fill

The segmentation of a point cloud into planar primitives is a popular approach to first-line scene interpretation and is particularly useful in mobile robotics for the extraction of drivable or walkable surfaces and for tabletop segmentation for manipulation purposes. Unfortunately, the planar segmentation task becomes particularly challenging when the point clouds are obtained from an inherently noisy, robot-mounted sensor that is often in motion, therefor requiring real time processing capabilities. We present a real time-capable plane segmentation technique based on a region growing algorithm that exploits the organized structure of point clouds obtained from RGB-D sensors. In order to counteract the sensor noise, we invest into careful selection of seeds that start the region growing and avoid the computation of surface normals whenever passible. We implemented our algorithm in C++ and thoroughly tested it in both simulated and real-world environments where we are able to compare our approach against existing state-of-the-art methods implemented in the Point Cloud Library. The experiments presented here suggest that our approach is accurate and fast, even in the presence of considerable sensor noise.