Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, localization is achieved by detecting and tracking visual features or landmarks, which provide information about the sensor's relative pose. For reliable feature tracking and accurate pose estimation, it is crucial to maintain visibility of a sufficient number of features. This requirement can sometimes conflict with the robot's overall task objective. In this paper, we approach it as a constrained control problem. By leveraging the invariance properties of visibility constraints within the robot's kinematic model, we propose a real-time safety filter based on quadratic programming. This filter takes a reference velocity command as input and produces a modified velocity that minimally deviates from the reference while ensuring the information score from the currently visible features remains above a user-specified threshold. Numerical simulations demonstrate that the proposed safety filter preserves the invariance condition and ensures the visibility of more features than the required minimum. We also validated its real-world performance by integrating it into a visual simultaneous localization and mapping (SLAM) algorithm, where it maintained high estimation quality in challenging environments, outperforming a simple tracking controller.

翻译：视觉传感器被广泛用于机器人位姿定位，特别是在全球定位工具（如GPS或运动捕捉系统）不可用的环境中。在许多视觉导航系统中，定位是通过检测和跟踪视觉特征或路标实现的，这些特征提供了传感器相对位姿的信息。为了确保可靠的特征跟踪和精确的位姿估计，保持足够数量特征的可见性至关重要。这一要求有时可能与机器人的整体任务目标产生冲突。在本文中，我们将其视为一个约束控制问题。通过利用机器人运动学模型中可见性约束的不变性特性，我们提出了一种基于二次规划的实时安全滤波器。该滤波器以参考速度指令作为输入，生成一个经过修正的速度输出，该输出在最小化与参考速度偏差的同时，确保当前可见特征的信息得分始终高于用户指定的阈值。数值仿真表明，所提出的安全滤波器保持了不变性条件，并确保了可见特征数量始终高于所需的最低限度。我们还通过将其集成到视觉同步定位与建图（SLAM）算法中验证了其实际性能，在具有挑战性的环境中，该滤波器保持了较高的估计质量，其表现优于简单的跟踪控制器。