Analogy-Augmented Uncertainty-Aware Monocular Visual Odometry

Visual odometry (VO) is a critical component of autonomous robot systems, enabling precise pose estimation from visual inputs. Learning-based VO methods are increasingly recognized for their robustness in challenging scenarios, including dynamic environments, motion blur, and low-light conditions. However, their performance is constrained by both the diversity of the data and its utilization rate. To overcome these limitations, we propose an end-to-end monocular VO system incorporating a novel learning-based end-to-end VO framework and multiple analogy augmentation strategies. We introduce the Context Attention Uncertainty-aware VO Network (CUVO), which prioritizes semantically rich regions and mitigating interference from high-uncertainty areas to enhance attentional focus and pose estimation accuracy. Furthermore, our analogy augmentation methods—temporal reversal, random rotation, and geometric mirroring—enhance image pairs and compute corresponding true pose transformations, significantly increasing training data quantity and diversity. Simultaneously, an analogous loss is applied to ensure consistency between the original and augmented data. Extensive experiments demonstrate that CUVO significantly enhances VO performance, outperforming previous end-to-end VO methods on TartanAir and KITTI datasets. By leveraging analogy augmentation strategy to expand training data under limited data conditions (27k), zero-shot capability of CUVO degrades by up to 29.5% on TartanAir and 23.3% on KITTI. Our work introduces the first image-to-pose data augmentation method tailored for VO and establishes CUVO as a robust system for advancing learning-based visual odometry.