A Comparison of Deep Learning-Based Monocular Visual Odometry Algorithms

Visual odometry (VO) has recently attracted significant attention, as evidenced by the increasing interest in the development of autonomous mobile robots and vehicles. Studies have traditionally focused on geometry-based VO algorithms. These algorithms exhibit robust results under a restrictive setup, such as static and well-textured scenes. However, they are not accurate in challenging environments, such as changing illumination and dynamic environments. In recent years, VO algorithms based on deep learning methods have been developed and studied to overcome these limitations. However, there remains a lack of literature that provides a thorough comparative analysis of state-of-the-art deep learning-based monocular VO algorithms in challenging environments. This paper presents a comparison of four state-of-the-art monocular VO algorithms based on deep learning (DeepVO, SfMLearner, SC-SfMLearner, and DF-VO) in environments with glass walls, illumination changes, and dynamic objects. These monocular VO algorithms are based on supervised, unsupervised, and self-supervised learning integrated with multiview geometry. Based on the results of the evaluation on a variety of datasets, we conclude that DF-VO is the most suitable algorithm for challenging real-world environments.