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.
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Zach, C. (2014). Robust Bundle Adjustment Revisited. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_50
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DOI: https://doi.org/10.1007/978-3-319-10602-1_50
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