In this article, the problem of distributed fault detection and isolation for single fault is considered for multi-agent systems affected by disturbances and communication delays. A bank of unknown input observers based on local information are constructed for distributed fault detection and isolation, and the sufficient conditions are presented to guarantee that the fault-detection residuals generated by unknown input observers satisfy [Formula: see text]. The logic of distributed fault detection is as follows: a agent can be declared as faulty when it's fault residuals exceed the threshold, and the fault residuals of its adjacent nodes are less than the threshold, declaring the existence of the fault.
A novel approach is presented here to solve the problem of motion occlusion and motion edge blurring in the existing scene flow estimation. First instance segmentation and superpixels are combined to segment the target and other regions in fusion segmentation. The pixels in each block are then redistributed by the optical flow to ensure the motion of pixels in the subblocks is consistent. Moreover, the 3D motion of subblocks with sufficient pixels is estimated by the energy function, and the others are considered outliers. Finally, the Driving and the KITTI benchmarks are used to evaluate the proposed method. The results demonstrated that the fusion of segmentation and redistribution is positive for the estimation, and this method outperforms the other state‐of‐the‐art methods both qualitatively and quantitatively.
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