Terrain Aided Planetary UAV Localization Based on Geo-referencing

The autonomous real-time optical navigation of planetary unmanned aerial vehicle (UAV) is of the key technologies to ensure the success of the exploration. In such a GPS-denied environment, vision-based localization is an optimal approach. In this article, we proposed a terrain aided simultaneous localisation and mapping (SLAM) algorithm, which simultaneously reconstructs the 3-D map point of environment and estimates the location of a planet UAV based on preexisting digital elevation model (DEM). To directly georeference the onboard UAV images to the digital terrain model, a theoretical model is proposed to prove that topographic features of UAV image and DEM can be correlated in the frequency domain via cross power spectrum. To provide the six-DOF of the UAV, we developed an optimization approach, which fuses the geo-referencing result into an SLAM system via local bundle adjustment (LBA) to achieve robust and accurate vision-based navigation even in featureless planetary areas. To test the robustness and effectiveness of the proposed localization algorithm, a new dataset for planetary drone navigation is proposed based on simulation engine. The proposed dataset includes 40 200 synthetic drone images taken from nine planetary scenes with related DEM query images. Comparison experiments are carried out to demonstrate that over the flight distance of 33.8 km, the proposed method achieved an average localization error of 0.45 m, compared to 1.32 m by ORB-SLAM2 and 0.75 m by ORB-SLAM3, with the processing speed of 12 Hz, which will ensure real-time performance. We will make our datasets available to encourage further work on this topic.