Drone Image Pose Estimation With Simultaneous Road Correspondence

Estimation of image or camera pose, including three position and three rotation parameters, is a fundamental task in remote sensing. When global navigation satellite system (GNSS) signal is unavailable or interferes with accurate pose, estimation is still challenging since its initials can be rather rough and the search space is large. Conventional solutions to such problems rely on ground control points that are identifiable in the images; however, this is an unrealistic or inconvenient requirement for many applications. We report a novel approach that uses a 3-D road database to determine the image pose without known image-road correspondence. After the road database is projected to images using the current estimated pose, we calculate a set of spectral and geometric road attributes for these projections. Taking advantage of the geometry of an image triplet, road projections from two neighboring images are transferred to the third one. For each image triplet, the final estimated pose minimizes the differences among the attributes of the road projections, extracted image roads, and transferred roads. Tests using two drone image datasets demonstrate that this approach can obtain an accurate pose at an uncertainty of less than 2.2 m and 2.6° compared to the bundle adjustment (BA) results. The use of an image triplet considerably improves the robustness and accuracy of independent single-image pose estimation. The developed visual hull quality metric can intrinsically determine the reliability of the estimated pose. This work will benefit image-based positioning and mapping applications where images have no ground control points or GNSS signals are degraded.