With the aim of providing a fast and effective segmentation method based on the flood-fill algorithm, in this study, we propose a new approach to segment a 3D point cloud gained by a 3D multi-channel laser range sensor into different objects. First, we divide the point cloud into two groups: ground and nonground points. Next, we segment clusters in each scanline dataset from the group of nonground points. Each scanline cluster is joined with other scanline clusters using the flood-fill algorithm. In this manner, each group of scanline clusters represents an object in the 3D environment. Finally, we obtain each object separately. Experiments show that our method has the ability to segment objects accurately and in real time.