Robust Bundle Adjustment Revisited^*

Christopher Zach

Abstract. In this work we address robust estimation in the bundle adjustment procedure. Typically, bundle adjustment is not solved via a generic optimization algorithm, but usually cast as a nonlinear least-squares problem instance. In order to handle gross outliers in bundle adjustment the least-squares formulation must be robustified. We investigate several approaches to make least-squares objectives robust while retaining the least-squares nature to use existing efficient solvers. In particular, we highlight a method based on lifting a robust cost function into a higher dimensional representation, and show how the lifted formulation is efficiently implemented in a Gauss-Newton framework. In our experiments the proposed lifting-based approach almost always yields the best (i.e. lowest) objectives.

Keywords: Bundle adjustment, nonlinear least-squares optimization, robust cost function

Electronic Supplementary Material:

• Electronic Supplementary Material (PDF 123 KB)

LNCS 8693, p. 772 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer International Publishing Switzerland 2014