Traditional feature-based monocular visual simultaneous localization and mapping (vSLAM) methods suffer from frequent tracking failure in low-texture scenes. Although tracking stability can be guaranteed by actively adjusting the camera’s orientation to track known landmarks, it can easily lead to the problem of over-exploitation. This means that instead of discovering new landmarks, the camera focuses on an area that has already been observed, which is not conducive to fully exploring unknown environments. To address this problem, an uncertainty-driven active view planning framework is proposed to actively adjust the orientation of the monocular camera equipped on a three degree of freedom (3-DoF) pan–tilt. As a result, a trade-off between exploitation, i.e., making full use of known information, and exploration, i.e., obtaining more information of unknown environments can be achieved. First, a novel landmark uncertainty model is established to represent the uncertainty of environmental information. Second, the trade-off problem is formulated as an inequality-constrained optimization mathematical model, whose objective function is related to landmark uncertainty. Last, Karush–Kuhn–Tucker (KKT) conditions are utilized to solve the optimization problem. Experimental results on a publicly available monocular dataset and in a real-world environment show that this framework reduces the rate of tracking failure by 50% on average. The localization error is also reduced by 0.07 m for translation and 0.004 rad/m for rotation on average. Meanwhile, the number of reconstructed landmarks increases by 17.86% on average, which indicates an appropriate trade-off between exploitation and exploration.

传统的基于特征的单眼视觉同时定位和制图（vSLAM）方法在低纹理场景中经常出现跟踪失败的情况。尽管可以通过主动调整相机的方向来跟踪已知的地标来保证跟踪的稳定性，但是它很容易导致过度开发的问题。这意味着相机不会发现新的地标，而是将焦点放在已经观察到的区域上，这不利于完全探索未知的环境。为了解决此问题，提出了一种不确定性驱动的主动视图计划框架，以主动调整配备有三自由度（3-DoF）旋转云台的单眼相机的方向。结果，在开发（即充分利用已知信息）和探索（即 可以获得未知环境的更多信息。首先，建立了一个新颖的地标不确定性模型来表示环境信息的不确定性。其次，权衡问题被公式化为一个不等式约束的优化数学模型，其目标函数与地标不确定性有关。最后，利用Karush–Kuhn–Tucker（KKT）条件来解决优化问题。在公开的单目数据集和真实环境中的实验结果表明，该框架将跟踪失败率平均降低了50％。平均而言，平移的定位误差也降低了0.07 m，旋转的定位误差平均降低了0.004 rad / m。同时，重建地标的数量平均增加了17.86％，