Scaling the world of monocular SLAM with INS-measurements for UAS navigation

The usage of a global positioning system (GPS) corrected inertial navigation system (INS) seems to be advantageous to camera-based pose estimation algorithms for outdoor navigation. The GPS signals lead to a geographical location with an accuracy of a few meters or even up to some centimeters in a setup utilizing correction data. Performing the pose estimation with a camera system has several disadvantages. The high amount of data has to be processed and the robustness may vary because of different environment and weather conditions. Nevertheless, cameras are the most used sensors for various tasks with unmanned aerial systems (UAS) because of their low cost and weight. The research results for vision-based simultaneous localization and mapping (SLAM) algorithms show the great capabilities of vision-based navigation. Therefore, the possibilities of cameras should be investigated for outdoor navigation in conjunction with an INS or as standalone backup solution in the case of an INS failure. The vision-based pose estimation using a SLAM algorithm provides considerable additional value by providing a map of the observed area. In contrast to keypoint-based approaches, the usage of a semi-dense formulation leads to 3D information for all image regions with gradients and results in a detailed 3D reconstruction. However, the main problem of the monocular SLAM remains the same. The visual pose estimations, and consequently the generated world, suffer from the unknown scale and the corresponding scale drift. In this work we develop a large scale semi-dense SLAM algorithm for the outdoor navigation of UAS equipped with a camera and an INS. This sensor combination increases the robustness of the visual pose estimation process and enables the generation of a very detailed, metric scaled map.