Depth images captured by off-the-shelf RGB-D cameras suffer from much stronger noise than color images. In this paper, we propose a method to denoise the depth images in RGB-D images by color-guided graph filtering. Our iterative method contains two components: color-guided similarity graph construction, and graph filtering on the depth signal. Implemented in graph vertex domain, filtering is accelerated as computation only occurs among neighboring vertices. Experimental results show that our method outperforms state-of-art depth image denoising methods significantly both on quality and efficiency.

A new predictive guidance law for moving-mass actuated reentry vehicle is presented in this paper. The proposed approach is based on the fact that lateral force is formed in a plane which is perpendicular to the reentry vehicle velocity. Particularly, the nominal impact point is defined as the impact point generated by zero lift trajectory. The virtual displacement is defined as the tiny change of the nominal impact point, which is caused by any instantaneous lateral force at the beginning of the trajectory. The optimal direction of the lateral force is obtained by the solution of optimal virtual displacement, in which the optimal virtual displacement should point to the target. The linear quadratic gauss (LQG) method and the analytical solution of the reentry trajectory are used in this guidance method. Simulation results demonstrate the effectiveness of the proposed method.

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