Abstract

In this work we address robust estimation in the bundle ad- justment 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 inves- tigate several approaches to make least-squares ob jectives robust while retaining the least-squares nature to use existing efficient solvers. In par- ticular, 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 experi- ments the proposed lifting-based approach almost always yields the best (i.e. lowest) ob jectives.