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As the rapid development of sensing and mapping techniques, it becomes a well-known technology that a map of complex environment can be generated using a robot carrying sensors. However, most of the existing researches represent environments directly using the integration of point clouds or other low-level geometric primitives. It remains an open problem to automatically convert these low-level map representations to semantic descriptions in order to effectively support high-level decision of a robot. Based on another representation of 3D point clouds, i.e. range image, this paper proposes a framework of segmentation and classification of range image, the objective of which is to annotate class labels to the data clusters that are obtained through a graph-based segmentation. Experimental results are presented and evaluated demonstrating that the proposed algorithm has efficiency in understanding the semantic knowledge of a large dynamic urban outdoor environment.