Monocular cameras are extensively employed in indoor robotics, but their performance is limited in visual odometry, depth estimation, and related applications due to the absence of scale information.Depth estimation refers to the process of estimating a dense depth map from the corresponding input image, existing researchers mostly address this issue through deep learning-based approaches, yet their inference speed is slow, leading to poor real-time capabilities. To tackle this challenge, we propose an explicit method for rapid monocular depth recovery specifically designed for corridor environments, leveraging the principles of nonlinear optimization. We adopt the virtual camera assumption to make full use of the prior geometric features of the scene. The depth estimation problem is transformed into an optimization problem by minimizing the geometric residual. Furthermore, a novel depth plane construction technique is introduced to categorize spatial points based on their possible depths, facilitating swift depth estimation in enclosed structural scenarios, such as corridors. We also propose a new corridor dataset, named Corr\_EH\_z, which contains images as captured by the UGV camera of a variety of corridors. An exhaustive set of experiments in different corridors reveal the efficacy of the proposed algorithm.

翻译：单目相机在室内机器人领域广泛应用，但由于缺乏尺度信息，其在视觉里程计、深度估计及相关应用中的性能受限。深度估计是指从输入图像中恢复稠密深度图的过程，现有研究者大多通过基于深度学习的方法解决该问题，但此类方法推理速度较慢，导致实时性不足。针对这一挑战，本文提出一种面向走廊环境的快速单目深度恢复显式方法，该方法基于非线性优化原理。通过采用虚拟相机假设，充分利用场景的几何先验特征，将深度估计问题转化为最小化几何残差的优化问题。此外，引入一种新颖的深度平面构建技术，根据空间点的可能深度对其进行分类，从而促进走廊等封闭结构场景中的快速深度估计。本文还提出一个新的走廊数据集Corr_EH_z，包含无人地面车辆在不同走廊环境中采集的图像。在多种走廊环境下的全面实验验证了所提算法的有效性。