CIBVS: Continuous Image-Based Visual Servoing Against Visual Signal Loss

Image-based visual servoing guides a robotic system to its desired place through ongoing feedback from visual signals. However, interference such as object occlusion and camera drift may cause visual signal loss, ultimately leading to servo failure. This article proposes a novel continuous image-based visual servoing (CIBVS) approach to address the potential visual signal loss. A geometric relationship is established between the positions of natural features and servo features. In each iteration, natural features are coarsely matched between frames, and mismatched pairs are eliminated by the proposed refining score based on the geometric relationship. When visual signal loss occurs, CIBVS leverages the natural features and the geometric relationship to estimate the lost servo features through optimization, ensuring continuous and stable visual feedback generation. In simulation, CIBVS was evaluated across three scenarios and 750 test cases, achieving an enhanced success rate and estimation accuracy. In the real world, CIBVS was integrated into various robotic systems, including manipulators, drones, and ground vehicles, to achieve multiple tasks. Experimental results demonstrated that, in scenarios with visual signal loss, CIBVS achieved significant improvements over extended-Kalman-filter-based IBVS and visual-odometry-based IBVS in servoing success rate, estimation accuracy, and convergence speed.