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A Structure discrimination method by deep learning with point cloud data

Tanifuji, Yuta ; Kawabata, Kuniaki  

This paper describes about the development of an environment recognition method with point cloud data collected in a dark place like Fukushima Daiichi Nuclear Power Station (FDNPS). We reported the results of a feasibility study of the structure discriminations from LiDAR 3D point cloud data by a deep learning approach. Proposed method utilizes the image data of projected 3D point cloud as input for the classifier instead of coordinate data of 3D points directly. This idea realized to make shorten the learning time without large capacity of the memory for the computations. We selected five kinds of structures (Stairs, Pipe, Grating, Switchboard and Valve) commonly appeared in the general plant as a discrimination subjects for evaluating proposed method. As a result, the classifier showed an accuracy of 99.6% to five categories and we could confirm the validity of proposed method for the structure discrimination.



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