LoSeVO: Local Sequence Constraints for Deep Visual Odometry

Many current visual odometry (VO) methods that utilize deep learning primarily concentrate on the constraints of motion relationships between adjacent frames, neglecting the modeling of temporal correlations within sequence data. Consequently, this paper introduces LoSeVO to effectively capture and model the temporal correlation features present in images. We design a Joint Feature Extraction component that not only performs joint feature extraction on adjacent frames but also extracts features from cross-frame images, which are called feature-guided maps. Then, we apply a Local Consistency Constraint component to the joint features between adjacent frames. It can adaptively constrain adjacent frames at different temporal positions within a sequence using different feature-guided maps. Extensive experiments based on the KITTI and Malaga datasets have shown that, compared to our previous DeepAVO model, LoSeVO can improve pose estimation performance by up to 23% and 9% in translation and rotation estimation, respectively.