An Efficient Bundle Adjustment Model Based on Parallax Parametrization for Environmental Monitoring

With the rapid development of Unmanned Aircraft Systems (UAS), more and more research fields have been successfully equipped with this mature technology, among which is environmental monitoring. One difficult task is how to acquire accurate position of ground object in order to reconstruct the scene more accurate. To handle this problem, we combine bundle adjustment method from Photogrammetry with parallax parametrization from Computer Vision to create a new method call APCP (aerial polar-coordinate photogrammetry). One impressive advantage of this method compared with traditional method is that the 3-dimensional point in space is represented using three angles (elevation angle, azimuth angle and parallax angle) rather than the XYZ value. As the basis for APCP, bundle adjustment could be used to optimize the UAS sensors' pose accurately, reconstruct the 3D models of environment, thus serving as the criterion of accurate position for monitoring. To verity the effectiveness of the proposed method, we test on several UAV dataset obtained by non-metric digital cameras with large attitude angles, and we find that our methods could achieve 1 or 2 times better efficiency with no loss of accuracy than traditional ones. For the classical nonlinear optimization of bundle adjustment model based on the rectangular coordinate, it suffers the problem of being seriously dependent on the initial values, making it unable to converge fast or converge to a stable state. On the contrary, APCP method could deal with quite complex condition of UAS when conducting monitoring as it represent the points in space with angles, including the condition that the sequential images focusing on one object have zero parallax angle. In brief, this paper presents the parameterization of 3D feature points based on APCP, and derives a full bundle adjustment model and the corresponding nonlinear optimization problems based on this method. In addition, we analyze the influence of convergence and dependence on the initial values through math formulas. At last this paper conducts experiments using real aviation data, and proves that the new model can effectively solve bottlenecks of the classical method in a certain degree, that is, this paper provides a new idea and solution for faster and more efficient environmental monitoring.