Fast point cloud segmentation for an intelligent vehicle using sweeping 2D laser scanners

The previously developed radially bounded nearest neighbor (RBNN) algorithm have been shown a good performance for 3D point cloud segmentation in indoor scenarios. In outdoor scenarios however it is hard to adapt the original RBNN to an intelligent vehicle directly due to several drawbacks. In this paper, drawbacks of RBNN are addressed and we propose an enhanced RBNN for an intelligent vehicle operating in urban environments by proposing the ground elimination and the distance-varying radius. After the ground removal, objects can be remained to segment without merging the ground and objects, whereas the original RBNN with the fixed radius induced over-segmentation or under-segmentation. We design the distance-varying radius which is varied properly from the distance between a laser scanner and scanning objects. The proposed distance-varying radius is successfully induced to segment objects without over or under segmentation. In the experimental results, we have shown that the enhance RBNN is preferable to segment urban structures in terms of time consumption, and even segmentation rates.