MD2VO: Enhancing Monocular Visual Odometry through Minimum Depth Difference

Abstract

Monocular visual odometry is an indispensable technique in visual signal tasks, including robotics and navigation. However, the scale ambiguity and poor generalizability of monocular systems remain significant challenges. To address this issue, we propose a novel framework to enhance visual odometry (VO) using monocular depth estimation algorithms and geometry-based methods. Our approach employs a depth point filtering method that utilizes depth information output from multiple depth estimation models with a minimum depth difference strategy. This approach effectively combines depth information from different models, resulting in more accurate scale factor recovery and trajectory prediction using PnP. Experiment results demonstrate that our method outperforms current learning-based VO methods in terms of generalization capability and accuracy. Additionally, we show that incorporating pre-trained models in the VO system to obtain pseudo-labeled depth data significantly enhances the performance of existing geometry-based VO methods.