Direct monocular simultaneous localization and mapping (SLAM) methods, for which the image intensity is used for tracking and mapping instead of sparse feature points, have gained in popularity in recent years. However, feature-based methods usually have more accurate camera localization results than most direct methods, though direct methods can work better in a textureless environment. To tackle the localization issue, we develop a novel real-time large-scale direct SLAM model, namely, GCP-SLAM, by integrating the learning-based confidence estimation into the depth fusion and motion tracking optimization. In GCP-SLAM, a random regression forest is trained off-line with pre-defined confidence measures for learning confidence and detecting the ground control points (GCPs). Then, the confidence value along with the selected GCPs is utilized for depth refinement and camera localization. Our proposed method is shown experimentally more reliable in tracking and relocalization than the previous state-of-the-art direct method when compared with feature-based and RGBD SLAMs.INDEX TERMS Depth estimation, ground control points, motion tracking, random forest, SLAM.

In this paper, a novel adaptive continuous sliding mode (SM) control approach is proposed based on the robust stability analysis of buck converters with multi-disturbances. Instead of the traditional first-order SM methods, the twisting algorithm is adopted to realize the control continuity and further weaken their inherent chattering problem. By introducing the zero-crossing detection innovatively, the influence of the traditional twisting algorithm with fixed control gain led to the low precision is effectively eliminated, and the small region can be controlled due to the implementation of the adaptive time-varying control gain. In addition, the magnitude of the controller can be reduced to a minimal admissible level determined by the system stable conditions and the selection of optimization parameters. Furthermore, multiple model uncertainties and external disturbances are considered into the modeling of the buck converters and the proposed adaptive SM controllers to ensure the strong robustness of the whole system while suffering possible disturbances. Finally, comparative simulations and experiments are given to validate the effectiveness of the proposed adaptive control strategy.INDEX TERMS Adaptive continuous sliding mode control, buck converters, zero-crossing detection, multidisturbances, twisting sliding mode control.

This paper investigates the stability problem of sliding mode controlled buck converters affected by unmodelled dynamics of circuit elements and Hall sensor. The parasitic resistors of all elements are contained in the modelling of buck converters. Different from the traditional sliding mode approach based on the nominal model, the circuit parasitic parameters are directly included in the controller design and stability analysis, which divide the regulation region located in the right half axis into four sub‐ranges. It is more accurate and has no need of extra compensator. For Hall sensor, singular perturbation theory is adopted for modelling and analysing, giving a stable condition concerning its dynamic and static parameters. Finally, the influence of the two types of unmodelled dynamics on the whole closed‐loop system is investigated by constructing an equivalent model, giving a more strict stability condition. Simulations and experiments are presented to illustrate the non‐negligible influence of these unmodelled dynamics.

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In this paper, a novel adaptive second-order sliding mode (2-SM) control approach, based on online zero-crossing detection, was proposed to solve the problems of the chattering and fixed control gain for buck converters with multi-disturbances. In modeling, the possible parameter perturbations and external disturbances of the converter system were contained. Instead of the traditional first-order sliding mode (1-SM), the twisting algorithm with 2-SM was adopted for the controller design, which could overcome the chattering problem and realize control continuity. Meanwhile, a novel adaptive mechanism was introduced to replace the conventional fixed control gain by time-varying control gain, the idea of which is to calculate the number of the zero-crossing points of the sliding surface online. As a result, the control magnitude of the improved controller could be reduced to a minimal admissible level, and the steady error of the output voltage could converge to the expected value. Furthermore, the robust stability of the converter system with multi-disturbances wads investigated. Comparative simulations and experiments validated the advantages of this paper as offering better robustness and control performance.

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Modal phase matching (MPM) is a widely used phase matching technique in AlxGa 1−x As and other χ (2) nonlinear waveguides for efficient wavelength conversions. The use of a non-fundamental spatial mode compensates the material dispersion but also reduces the spatial overlap of the three interacting waves and therefore limits the conversion efficiency. In this work, we develop a technique to increase the nonlinear overlap by modifying the material nonlinearity, instead of the traditional method of optimizing the modal field profiles. This could eliminate the limiting factor of low spatial overlap inherent to MPM and significantly enhance the conversion efficiency. Among the design examples provided, this technique could increase the conversion efficiency by a factor of up to ∼290 in an AlxGa 1−x As waveguide. We further show that this technique is applicable to all χ (2) material systems that utilize MPM for wavelength conversion.