Spatio-temporal and geometry constrained network for automobile visual odometry

Visual odometry (VO) is an essence of vision-based localization and mapping system where existing learning-based approaches utilize CNN and RNN to model camera motion and gain promising results. However, these methods lack full use of the relationship between spatial characteristics and temporal clues, as well as geometry constraints in VO. To overcome these deficiencies, an end-to-end framework that leverages spatio-temporal relevance and geometrical knowledge is proposed. In particular, a spatial response module (SRM) is designed to extract the visual motion features by emphasizing the most interconnected regions while suppressing the irrelevant areas. A module named temporal response module (TRM) is used to regress the camera motion via adopting the optimal motion features. Moreover, a geometry constrained (GC) loss that minimizes the estimated inter-frame pose errors and the accumulated pose errors within a local period is introduced. Actually, the GC loss utilizes adaptive learnable balance factors for balancing losses. Experimental results on KITTI and Malaga datasets demonstrate that the proposed model outperforms state-of-the-art monocular methods.