Precise Vision-Aided Aerial Navigation

This paper proposes a novel vision-aided navigation approach that continuously estimates precise 3D absolute pose for aerial vehicles, using only inertial measurements and monocular camera observations. Our approach is able to provide accurate navigation solutions under long-term GPS outage, by tightly incorporating absolute geo-registered information into two kinds of visual measurements: 2D-3D tiepoints, and geo-registered feature tracks. 2D-3D tie-points are established by finding feature correspondences to align an aerial video frame to a 2D geo-referenced image rendered from the 3D terrain database. These measurements provide global information to correct accumulated error in navigation estimation. Geo-registered feature tracks are generated by associating features across consecutive frames. They enable the propagation of 3D geo-referenced values to further improve the pose estimation. All sensor measurements are fully optimized in a smoother-based inference framework, which achieves efficient relinearization and real-time estimation of navigation states and their covariances over a constant-length of sliding window. Experimental results demonstrate that our approach provides accurate and consistent aerial navigation solutions on several large-scale GPS-denied scenarios.