LAUSR

ABSTRACT This letter introduces an efficient bundle adjustment (BA) method based on the sparse Broyden-Fletcher-Goldfarb-Shanno (sparse BFGS, sBFGS) solution, which efficiently estimates camera poses and 3-D points. The Levenberg-Marquardt (LM) solution, which is widely applied in BA problems, requires more linear equation solution and more iterations. A gain matrix calculated using both the Jacobian matrix and residual vector replaces the simple diagonal matrix of the LM solution, which results in a better estimation of the descent direction and step size when finding a path to a local minimum in a sparse BFGS solution. Four datasets were verified, and the results demonstrate that the proposed method requires fewer linear equation solutions to converge to a minimum compared with that of the LM-based BA method.