A self-adaptation feature correspondences identification algorithm in terms of IMU-aided information fusion for VINS

Feature correspondences identification between consecutive frames is a critical prerequisite in the monocular Visual-Inertial Navigation System (VINS). In this paper, we propose a novel self-adaptation feature point correspondences identification algorithm in terms of IMU-aided information fusion at the level of feature tracking for nonlinear optimization framework-based VINS. This method starts with an IMU pre-integration predictor to predict the pose of each new coming frame. In weak texture scenes and motion blur situations, in order to increase the number of feature correspondences and improve the track lengths of feature points, we introduce a novel predicting-matching based feature point tracking strategy to build new matches. On the other hand, the predicted pose is incorporated into the outliers rejection step to deal with mismatch caused by dynamic objects. Finally, the proposed self-adaptation feature correspondences identification algorithm is implemented based on VINS-Fusion and validated through public datasets. The experimental results show that it effectively improves the accuracy and tracking length of feature matching, and demonstrates better performance in terms of camera pose estimation as compared to state-of-the-art approaches.