A new approach of point cloud processing and scene segmentation for guiding the visually impaired

Point clouds of 3D scenes are widely applied in guiding the visually impaired by precious research. Many auxiliary systems for the visually impaired are integrated with RGB-D sensors such as Kinect and binocular cameras, which are able to acquire depth pictures and 3D point clouds. Real-time location of objects is adjusted to the world coordinate system through utilization of attitude angle transducers. This paper proposed a novel approach of scene segmentation based on the estimation of normal vectors of a point cloud. Multiplying a point cloud's normal vectors in two directions helps to eliminate correlation in different directions. It is used to split a stereo scene into several surfaces such as ground, walls and slopes. The method is faster and can obtain more separated results than RANSAC algorithm. Besides, three ways to evaluate surface smoothness are compared, including inconsistent degree of normal vectors, variance of depths and difference between normal vectors of two sizes of adjacent regions. Experimental results attained from indoor and outdoor circumstances are presented to validate the approach. It is demonstrated that the proposed method can be efficiently applied into scene segmentation and guiding the visually impaired.