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Dynamic Covariance Scaling for Robust Map Optimization
Pratik Agarwal, Gian Diego Tipaldi, Luciano Spinello, Cyrill Stachniss, Wolfram Burgard
Workshop on robust and Multimodal Inference in Factor Graphs (ICRA)

Developing the perfect SLAM front-end that produces graphs which are free of outliers is hard to achieve due to perceptual aliasing. Converging to the correct solution is challenging for non-linear error minimization SLAM techniques even in the absence of outliers, if the initial guess is far away from the correct solution. Therefore, optimization back-ends need to be resilient to outliers resulting from an imperfect front-end as well as be robust to bad initialization. In this paper, we present dynamic covariance scaling, a novel approach for effective optimization of constraint networks under the presence of outliers and bad initial guess. The key idea is to use a robust function that generalizes classical gating and down-weights outliers without compromising convergence speed. Compared to recently published state-of-the-art methods, we obtain a substantial speed-up without increasing overheads.

  author = {Pratik Agarwal and Gian Diego Tipaldi and Luciano Spinello and Cyrill Stachniss and Wolfram Burgard},
  title = {Dynamic Covariance Scaling for Robust Robotic
  booktitle = {Workshop on robust and Multimodal Inference in
Factor Graphs, {ICRA}},
  year = 2013,
  month = may,
  doi = {10.1109/ICRA.2013.6630557},
  url = {},
  keyword = {SLAM, Robust optimization},
  address = {Karlsruhe, Germany}