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Wei Song 1 and Seoungjae Cho 2 and Kyungeun Cho 3 and Kyhyun Um 3 and Chee Sun Won 4 and Sungdae Sim 5
Academic Editor:Hwa-Young Jeong
1, Department of Computer Science & Technology, North China University of Technology, Beijing 100-144, China
2, Department of Multimedia Engineering, Graduate School of Dongguk University-Seoul, Seoul 100-715, Republic of Korea
3, Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 100-715, Republic of Korea
4, Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 100-715, Republic of Korea
5, Agency for Defense Development, Daejeon 305-152, Republic of Korea
Received 15 November 2013; Accepted 3 March 2014; 3 April 2014
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Mobile robots must be able to navigate and interact with unknown environments, without collisions or encountering other dangers, by determining traversable terrain regions, reconstructing terrain models, and employing other technologies. The technologies of dynamic terrain reconstruction and modeling with multiple sensors have been researched to provide mobile robots with the ability to conduct free space detection and support navigation without collision [1].
Datasets received by multiple sensors are integrated to produce accurate and reliable terrain information, such as 3D point cloud, video image, GPS data, and rotation state [2, 3]. It is necessary to integrate instantaneously received datasets into terrain model, such as voxel map and textured mesh. The conventional terrain modeling methods are difficult to process large-scale datasets, which are so large that they exceed the memory capacity of mobile robots. Meanwhile, the huge computational cost of the large-scale datasets processing causes a low speed of terrain modeling and visualization.
In remote operation applications, ground segmentation is necessary in the assessment of traversable regions. To accomplish this objective, we apply a ground segmentation method using the Gibbs-Markov random field (MRF) method. To create a photorealistic visualization for traversable ground surface, textured terrain models are represented using sensed datasets from each frame. By mapping captured video images on the 3D ground surface mesh, the visualization system provides intuitive imagery of a 3D geometric model for easy terrain perception. Conventionally, the captured images are...