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Robust Bundle Adjustment Revisited

  • Christopher Zach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

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 

Supplementary material

978-3-319-10602-1_50_MOESM1_ESM.pdf (122 kb)
Electronic Supplementary Material (PDF 123 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christopher Zach
    • 1
  1. 1.Toshiba Research EuropeCambridgeUK

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