Classification of 3-D point cloud data that includes line and frame objects on the basis of geometrical features and the pass rate of laser rays

Abstract

The authors aim at classification of 3-D point cloud data at disaster environment. In this paper, we proposed a method of classification for 3-D point cloud data using geometrical features and the pass rate of laser rays. Line and frame objects often trap robots, which causes the damages of sensors, motors, mechanical parts etc. at remote operation. Using our proposed method, the line and frame objects can be classified from the 3-D point cloud data. Key-point is use of the pass rate of laser rays. It is confirm that recognition rate of line and frame objects can be increased using the pass rate of laser rays. In addition, it is confirm that the proposed classification method works in the real scene. A training facility of Japan fireman department is used for the evaluation test because it is similar to the real disaster scene comparing the laboratory's test field. © Springer-Verlag Berlin Heidelberg 2014.