## 1 Introduction
One key issue is that IBVS often results in an overdetermined system, where the number of visual features exceeds the number of joint variables available for adjustment. For example, at least four 2D-3D correspondences are needed for a unique pose solution [6], but a camera's full six-degree-of-freedom (6-DOF) pose means that a 6-DOF robot may need to align itself with eight or more observed features. In traditional IBVS [15], the interaction matrix (or image Jacobian) defines the relationship between feature changes and joint velocities. When the system is overdetermined, this matrix contains more constraints than joint variables, leading to redundant information. Research [15] suggests that this redundancy may cause the camera to converge to local minima, failing to reach the desired pose. Although local asymptotic stability is always ensured in IBVS, global asymptotic stability cannot be guaranteed when the system is overdetermined. In recent research on visual servoing, various methods have been developed to address the limitations of traditional Image-Based Visual Servoing (IBVS), particularly the issue of local minima due to system overdetermination. [PERSON] et al. [21] proposed a hybrid switched-system control system, which alternates between IBVS and Position-Based Visual Servoing (PBVS) modes based on Lyapunov functions. This method offers formal local stability and effectively prevents loss of visual features or divergence from the goal. However, it may suffer from discontinuities near switching surfaces and is limited by local convergence guarantees. [PERSON] et al. [22] developed a 2% D visual servoing method, which integrates both image and pose features into a block-triangular Jacobian structure, allowing for smoother and more predictable camera trajectories while avoiding singularities. Yet, it is sensitive to depth estimation errors and requires careful selection of hybrid features. [PERSON] et al. [23] introduced an MPC-based IBVS framework for quadrotors, which leverages model predictive control to handle under-actuation while tracking IBVS commands. It provides formal stability guarantees and supports constraint handling in real time, but its reliance on linearization around hover and the absence of direct coupling with visual dynamics limit its generality and precision in highly nonlinear settings. While these methods achieve significant improvements in most scenarios, they also introduce computational challenges compared to traditional IBVS. The switched control method requires different control strategies tailored to specific dynamics, increasing the complexity of the overall control architecture [24]. The 2-1/2-D visual servoing method demands real-time processing of both visual and positional data, which can impose significant computational loads and limit performance in dynamic environments [25]. MPC approaches introduce additional computational overhead by requiring complex optimization at every time step, making real-time implementation costly [26]. In this paper, we focus on the PnP framework for determining and controlling the pose of a stereo camera within an image-based visual servoing (IBVS) architecture. In traditional IBVS, depth information between objects and the image plane is crucial for developing the interaction matrix. However, with a monocular camera, depth can only be estimated or approximated using various algorithms [15], and inaccurate depth estimation may lead to system instability. In contrast, a stereo camera system can directly measure depth through disparity between two image planes, enhancing system stability. A key novelty of this paper is providing systematic proof that stereo camera pose determination in IBVS can be formulated as a Perspective-3-Point (P3P) problem, which, to the best of our knowledge, has not been explored in previous research.