RBA: Reduced Bundle Adjustment for oblique aerial photogrammetry

Abstract

This study proposes an efficient Bundle Adjustment (BA) model for oblique aerial photogrammetry to reduce the number of unknown parameters and the dimensions of a non-linear optimization problem. Instead of serving as independent exterior orientations, oblique camera poses are parameterized with nadir camera poses and constant relative poses between oblique and nadir cameras. New observation functions are created with image points as a function of the nadir pose and the relative pose parameters. With these observation functions, the problem of BA is defined as finding optimal unknown parameters by minimizing the total difference between estimated and observed image points. A Gauss-Newton optimization method is utilized to provide a solution for this least-square problem with a reduced normal equation, which plays a very critical role in the convergence and efficiency of BA. Compared with traditional BA methods, the number of unknown parameters and the dimension of the normal equations decrease, this approach dramatically reduces the computational complexity and memory cost especially for large-scale scenarios with a number of oblique images. Four synthetic datasets and a real dataset were used to check the validation and the accuracy of the proposed method. The accuracy of the proposed method is very close to that of the traditional BA method, but the efficiency can be significantly improved by the proposed method. For very large-scale scenarios, the proposed method can still address the limitation of memory and orientate all images captured by an oblique aerial multi-camera system.