Simultaneous detection of obstacles in the near field of the mobile robot is extremely urgent and complex task. In general, environment around robot is very complicated due to differences in change of lighting conditi...
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ISBN:
(纸本)9783319237664;9783319237657
Simultaneous detection of obstacles in the near field of the mobile robot is extremely urgent and complex task. In general, environment around robot is very complicated due to differences in change of lighting conditions and viewing angles. These problems lead to decrease accuracy of obstacles recognition. They can change quickly. However, obstacles must occupy a certain region in space. Kinect can be used for obtaining depth information. Kinect is a new camera system. The high cost of the depth-sensor for a long time prevented active implementation of these methods. However, with the appearance Microsoft Kinect can significantly be improved quality and efficiency of obstacles detection in autonomous robot navigation. Obtained depth images have normal resolution of 640x480 pixels at 30 frames per second. In this paper, we propose algorithms for obstacle detection with algorithm "3D-point cloud". algorithm contains some main steps: Creation 3D-point cloud, transformation "2D-Point Cloud", obtaining results. Obstacles are detected based on their specific areas. 3D-point cloud are obtained from the depth data. Point clouds are usually defined as a set of unorganized irregular points in 3D. They not only save coordinates of all points in the near zone of mobile robot, but also additional data points (e.g., RGB color values, intensity, etc.). algorithm clustering (DBSCAN) has shown in step "getting results" to increase accuracy. Obstacle detection efficiency is increased by using point clouds due to lower size of input data. Using 3D-point clouds allows satisfying requirements for obstacle detection in real time. The experimental results show the effectiveness of proposed approach using in vision system of a mobile robot platform.
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