Camera Ego-Positioning Using Sensor Fusion and Complementary Method

Visual simultaneous localization and mapping (SLAM) is a common solution for camera ego-positioning. However, SLAM sometimes loses tracking, for instance due to fast camera motion or featureless or repetitive environments. To account for the limitations of visual SLAM, we use sensor fusion method to fuse the visual positioning results with inertial measurement unit (IMU) data based on filter-based, loosely-coupled sensor fusion methods, and further combines feature-based SLAM with direct SLAM via proposed complementary fusion to retain the advantages of both methods; i.e., we not only keep the accurate positioning of feature-based SLAM but also account for its difficulty with featureless scenes by direct SLAM. Experimental results show that the proposed complementary method improves the positioning accuracy of conventional vision-only SLAM and leads to more robust positioning results. ? 2021, Springer Nature Switzerland AG.