Clustering of Lidar Data Using Particle Swarm Optimization Algorithm in Urban Area

One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves their segmentation into consistent units through a clustering process. Nevertheless, due to the scene complexity and the variety of objects in urban area, e.g. buildings, roads, and trees, it is clear that a clustering using only a single cue will not suffice. Considering the availability of additional data sources, like laser range and intensity information in both first and last echo, more information can be integrated in the clustering process and ultimately into the recognition and reconstruction scheme. Multi dimensionality nature of LIDAR data with a dense sampling interval in urban area generates a huge amount of information. This amount of information has produced a lot of problems for finding global optima in most of traditional clustering techniques. This paper describes the potential of a Particle Swarm Optimization (PSO) algorithm to find global solutions to the clustering problem of multi dimensional LIDAR data in urban area. It is a kind of swarm intelligence that is based on social-psychological principles and provides insights into social behaviour, as well as contributing to engineering applications. By integrating the simplicity of the k-means algorithm with the capability of the PSO algorithm, this paper presents a robust and efficient clustering method which can overcome the problem of trapping to local optima of k-means technique. This algorithm successfully applied to clustering of several LIDAR data sets in different urban area with different size and complexities. The experimental results demonstrate that PSO based clustering technique produces much better outputs in terms of both accuracy and computation time than other traditional clustering techniques.