Fast Range Image-Based Segmentation Of Sparse 3d Laser Scans For Online Operation

Object segmentation from 3D range data is an important topic in mobile robotics. A robot navigating in a dynamic environment needs to be aware of objects that might change or move. A segmentation of the laser scans into individual objects is typically the first processing step before a further analysis is performed. In this paper, we present a fast method that segments 3D range data into different objects, runs online, and has small computational demands. Our approach avoids the explicit computation of the 3D point cloud and performs all computations directly on a 2D range image, which enables a fast segmentation for each scan. A further relevant aspect of our method is that we can segment objects even if the 3D data is sparse. This is important for scanners such as the new Velodyne Puck. We implemented our approach in C++ and ROS and thoroughly tested it using different 3D scanners. Our method can operate at over 100 Hz for the 64-beam Velodyne scanner on a single core of a mobile CPU while producing high quality segmentation results. In addition to this, we make the source code for the approach available.