A grid-point detection method based on U-net for a structured light system

Accurate detection of the feature points of the projected pattern plays an extremely important role in one-shot 3D reconstruction systems, especially for the ones using a grid pattern. To solve this problem, this paper proposes a grid-point detection method based on U-net. A specific dataset is designed that includes the images captured with the two-shot imaging method and the ones acquired with the one-shot imaging method. Among them, the images in the first group after labeled as the ground truth images and the images captured at the same pose with the one-shot method are cut into small patches with the size of 64x64 pixels then feed to the training set. The remaining of the images in the second group is the test set. The experimental results show that our method can achieve a better detecting performance with higher accuracy in comparison with the previous methods.Comment: http://airccse.org/csit/V10N16.htm

Robust control for a wheeled mobile robot to track a predefined trajectory in the presence of unknown wheel slips

In this paper, a robust controller for a nonholonomic wheeled mobile robot (WMR) is proposed for tracking a predefined trajectory in the presence of unknown wheel slips, bounded external disturbances, and model uncertainties. The whole control system consists of two closed loops. Specifically, the outer one is employed to control the kinematics, and the inner one is used to control the dynamics. The output of kinematic controller is adopted as the input of the inner (dynamic) closed loop. Furthermore, two robust techniques were utilized to assure the robustness. In particular, one is used in the kinematic controller to compensate the harmful effects of the unknown wheel slips, and the other is used in the dynamic controller to overcome the model uncertainties and bounded external disturbances. Thanks to this proposed controller, a desired tracking performance in which tracking errors converge asymptotically to zero is obtained. According to Lyapunov theory and LaSalle extension, the desired tracking performance is guaranteed to be achieved. The results of computer simulation have shown the validity and efficiency of the proposed controller

Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks

The one-shot structured light method using a color stripe pattern can provide a dense point cloud in a short time. However, the influence of noise and the complex characteristics of scenes still make the task of detecting the color stripe edges in deformed pattern images difficult. To overcome these challenges, a color structured light stripe edge detection method based on generative adversarial networks, which is named horizontal elastomeric attention residual Unet-based GAN (HEAR-GAN), is proposed in this paper. Additionally, a De Bruijn sequence-based color stripe pattern and a multi-slit binary pattern are designed. In our dataset, selecting the multi-slit pattern images as ground-truth images not only reduces the labor of manual annotation but also enhances the quality of the training set. With the proposed network, our method converts the task of detecting edges in color stripe pattern images into detecting centerlines in curved line images. The experimental results show that the proposed method can overcome the above challenges, and thus, most of the edges in the color stripe pattern images are detected. In addition, the comparison results demonstrate that our method can achieve a higher performance of color stripe segmentation with higher pixel location accuracy than other edge detection methods