Feature Assisted Direct Visual Odometry with Virtual Wide Field-of- View Tracking

Visual odometry systems are usually categorized as feature-based visual odometry systems and direct visual odometry systems. The feature-based visual odometry systems spend time on calculating descriptors while direct visual odometry systems directly calculate residuals. The optimization of direct visual odometry systems is sensitive with initial states while feature-based visual odometry systems did not suffer from this problem. This paper presents a features assisted stereo direct visual odometry system with virtual wide field-of-view tracking. It combines the advantages of feature-based methods and direct methods. It is achieved by incorporating a feature assisted predicting module and a virtual wide field-of-view tracking module into stereo direct sparse odometry (SDSO) framework. The feature-based matching is combined in direct tracking module for efficient and robust data association. Furthermore, before data association, a virtual wide field-of-view inverse depth frame is created in case of tracking lost in aggressive rotating conditions. Evaluation results on the EuRoC datasets and the KITTI dataset show that, with the aid of the new tracking module, the stereo visual odometry becomes more robust and more accurate.