Object Extraction from Lidar Data Using an Artificial Swarm Bee Colony Clustering Algorithm

Light Detection and Ranging (LIDAR) systems have become a standard data collection technology for capturing object surface information and 3D modeling of urban areas. Although, LIDAR systems provide detailed valuable geometric information, they still require extensive interpretation of their data for object extraction and recognition to make it practical for mapping purposes. A fundamental step in the transformation of the LIDAR data into objects is the segmentation of LIDAR data through a clustering process. Nevertheless, due to scene complexity and the variety of objects in urban areas, e.g. buildings, roads, and trees, clustering using only one single cue will not reach meaningful results. The multi dimensionality nature of LIDAR data, e.g. laser range and intensity information in both first and last echo, allow the use of more information in the data clustering process and ultimately into the reconstruction scheme. Multi dimensionality nature of LIDAR data with a dense sampling interval in urban applications, provide a huge amount of valuable information. However, this amount of information produces a lot of problems for traditional clustering techniques. This paper describes the potential of an artificial swarm bee colony optimization algorithm to find global solutions to the clustering problem of multi dimensional LIDAR data in urban areas. The artificial bee colony algorithm performs neighborhood search combined with random search in a way that is reminiscent of the food foraging behavior of swarms of honey bees. Hence, by integrating the simplicity of the k-means algorithm with the capability of the artificial bee colony algorithm, a robust and efficient clustering method for object extraction from LIDAR data is presented in this paper. This algorithm successfully applied to different LIDAR data sets in different urban areas with different size and complexities.