An Explicit Method for Fast Monocular Depth Recovery in Corridor Environments

TL;DR

This paper introduces a fast, explicit monocular depth recovery method tailored for corridor environments, utilizing geometric optimization and a novel depth plane technique to improve real-time depth estimation.

Contribution

The paper presents a new explicit optimization-based approach for monocular depth estimation in corridors, incorporating a depth plane construction method and a dedicated corridor dataset.

Findings

Achieves rapid depth estimation suitable for real-time applications

Demonstrates high accuracy in corridor environments

Provides a new dataset for corridor depth estimation

Abstract

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.