People are paying more and more attention to the use of Spherical Panorama Images (SPIs) for many applications. In order to apply SPIs in photogrammetric application such as bundle adjustment or land mapping like frame images do, conjugate points matching and the relative relationship between SPIs are important issues.

Through observing the moving pattern of feature points, the relative positions and orientation between camera stations may be solved. In this study, there are three test cases using Ladybug 5 system developed by Point Grey including camera movement, self-rotation and oblique. Image features are extracted and matched by Speed-Up Robust Features (SURF) algorithm (Bay, 2008), and the concept of Random Sample Consensus (RANSAC) is applied to improve the accuracy of conjugate feature points matching. Although RANSAC general model is not well enough to detect the features on spherical panorama images, we proposed a method using Essential Matrix model to improve this deficiency.

Once the conjugate points are found, the relationship between image stations can be explained by Essential matrix base on the coplanar condition and SVD decomposition. From the estimation of Essential Matrix, the rotation and translation parts can be extracted up to scale. Similar to that of frame camera, there are four possible solutions, the angle between two image stations is used to judge the correct solution.