Enhanced Clustering Method using 3D Laser Range Data for an Autonomous Vehicle

This paper describes an enhanced clustering method for 3D point cloud data which is acquired from a laser range scanning system. The proposed method overcomes the instinctive problems of the vertical or horizontal scanning system. The acquired 3D laser range data for an autonomous vehicle has a disadvantage in that the point cloud data from the system is not equally distributed based on distance. Moreover, the data information describes only the object’s shown surface. The proposed method uses essentially a radially bounded nearest neighbor approach and describes the definition of nearest neighbor points on the point cloud data from the scanning system. The method uses a Gaussian-based approach to find nearest neighbor points during the clustering process. In conclusion, the experimental results for the clustering and segmentation are shown with real data. Key–Words: 3D point cloud data, clustering, segmentation, autonomous vehicle, object recognition.