GraphAVO: Self-Supervised Visual Odometry Based on Graph-Assisted Geometric Consistency

Learning-based monocular visual odometry (VO) has recently attracted considerable attention for its robustness to camera parameters and environmental variations. Despite traditional pose graph optimization enhancing pose accuracy, its integration with deep learning may lead to error accumulation due to insufficient motion information exchange. Our method, GraphAVO, concentrates simultaneously on the adjacent and interval co-visibility correspondence to establish a feature and pose graph optimization for pose consistency. We design a graph-assisted Windowed Feature Graph Refinement (WFGR) component to operationalize pose graph optimization for deep feature refinement. The geometric consistency is further constrained by a Cycle Consistency Loss. Additionally, the Cascade Dilated Convolution Fusion (CDCF) component is incorporated to handle different degrees of pixel movement, facilitating the joint detection of slight and distinct motion cues for subsequent feature enhancement. Extensive experiments on the KITTI, Malaga, RobotCar, and self-collected outdoor datasets have demonstrated the promising performance and generalization ability of GraphAVO. It achieves competitive results against classical algorithms and outperforms related state-of-the-art methods by up to 24.4% and 40.1% on average translational and rotational evaluation, respectively.