The new adaptive clustering method of laser scanner data for automated vehicle obstacle recognition in unstructured environment

Clustering of laser scanner data is a key part of obstacle recognition of automated vehicle with laser scanner by which efficient obstacle determination and fast environment understanding will be achieved through the analysis of the several classes not amounts of data. Traditional clustering methods of laser scanner data based on hard-threshold principle can not meet the requirements of unstructured environment where the topography is complicated and changeable; unknown obstacles are not only complex but also various in kinds. A new adaptive clustering method of laser scanner data is presented in this paper. Little probability event principle is introduced to the nearest adjacent point clustering where every threshold is not fixed and set in advance but is obtained adaptively and effectively by little probability event principle which can reflect the overall change of a class. The new adaptive clustering method is applied for the clustering of ibeo LUX2010 laser scanner data for automated vehicle obstacle recognition in unstructured environment considering both the experimental conditions and the own characteristics of laser scanner. Experimental results show that, compared with the traditional nearest adjacent point clustering based on hard threshold principle, the new adaptive method can characterize the obstacle and the environment information by classes more efficiently and better in unstructured environment meanwhile keep fast enough to meet the real time request of the recognition system.