The Sparse Bundle Adjustment (SBA) algorithm is a widely used method to solve multi-view reconstruction problems in vision. The critical cost of SBA depends on the fill in of the reduced camera matrix whose pattern is known as the Secondary structure of the problem. In centered object applications where a large number of images are taken in a small area the camera matrix obtained when points are eliminated is dense. On the contrary, visual mapping systems where long trajectories are traversed yield sparse matrices. In this paper, we propose a Decomposable Bundle Adjustment (DBA) method which naturally adapts to the fill in pattern of the camera matrix improving the performance on visual mapping systems. The proposed algorithm is able to decompose the normal equations into small subsystems which are ordered in a junction tree structure. To solve the original system, local factorizations of the small dense matrices are passed between clusters in the tree. The DBA algorithm has been tested for simulated and real data experiments for different environment configurations showing good performance.