Abstract
Full autonomous robots in urban search and rescue (USAR) have to deal with complex terrains. The real-time recognition of terrains in front could effectively improve the ability of pass for rescue robots. This paper presents a real-time terrain classification system by using a 3D LIDAR on a custom designed rescue robot. Firstly, the LIDAR state estimation and point cloud registration are running in parallel to extract the test lane region. Secondly, normal aligned radial feature (NARF) is extracted and downscaled by a distance based weighting method. Finally, an extreme learning machine (ELM) classifier is designed to recognize the types of terrains. Experimental results demonstrate the effectiveness of the proposed system.
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Acknowledgements
Our work is supported by National Science Foundation of China (NO. 61503401 and NO. 61403409), China Postdoctoral Science Foundation (NO. 2014M562648), and graduate school of National University of Defense Technology. All members of the NuBot research group are gratefully acknowledged.
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Zhong, Y., Xiao, J., Lu, H., Zhang, H. (2017). Real-Time Terrain Classification for Rescue Robot Based on Extreme Learning Machine. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_38
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DOI: https://doi.org/10.1007/978-981-10-5230-9_38
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